Computer-implemented method, system, and computer program (automatic extension of program data storage)

Abstract

To provide a method for determining that data storage is insufficient for a program.SOLUTION: A processor may receive a set of marked data associated with a set of source codes. The processor may identify a scope of the codes associated with the marked data. The processor may generate a first set of instructions for taking a first snapshot of a variable storage space associated with the marked data when entering the scope and a second snapshot of the variable storage space when exiting the scope. The processor may generate a second set of instructions for comparing the first and second snapshots to determine a usage amount of the variable storage. The processor may generate a third set of instructions for performing an action in response to the usage amount of the variable storage space exceeding a threshold. The processor may compile the codes into an executable file.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] TECHNICAL FIELD The present disclosure relates generally to the field of data storage, and more specifically to automatically generating data usage statements that are used to determine when data storage becomes insufficient for a respective computer program. Summary of the Invention [Problem to be solved by the invention]

[0002] Computer programs are, to some extent, a reflection of the real world: some programs have a much longer lifespan than predicted, resulting in unexpected program instability when used for current day-to-day processing tasks. [Means for solving the problem]

[0003] Embodiments of the present disclosure include methods, computer program products, and systems for automatically determining when data storage becomes insufficient for a respective computer program. A processor may receive a set of marked data associated with a set of source code. The processor may identify a range of source code associated with the marked data. The processor may generate a first set of instructions for taking a first snapshot of variable storage space associated with the marked data when entering the range and a second snapshot of the variable storage space when leaving the range. The processor may generate a second set of instructions for comparing the first and second snapshots to determine usage of the variable storage space associated with the marked data. The processor may generate a third set of instructions for performing an action in response to usage of the variable storage space exceeding a threshold. The processor may compile the source code into an executable file. The executable file may include the first, second, and third sets of instructions.

[0004] The above summary is not intended to describe each illustrated embodiment or every implementation of the present disclosure. [Brief explanation of the drawings]

[0005] The drawings included in this disclosure are incorporated in and form a part of the specification. They illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The drawings are only illustrative of typical embodiments and are not intended to limit the present disclosure.

[0006] [Figure 1] FIG. 1 illustrates a block diagram of an exemplary system according to an embodiment of the present disclosure.

[0007] [Figure 2] FIG. 1 illustrates a block diagram of an exemplary compiler according to an embodiment of the present disclosure.

[0008] [Figure 3] FIG. 1 illustrates a flow diagram of an exemplary process for automatically generating a data usage statement and notifying a user when a data usage threshold is exceeded, according to an embodiment of the present disclosure.

[0009] [Figure 4] 1 illustrates an exemplary set of marked data and source code according to an embodiment of the present disclosure.

[0010] [Figure 5] 1 illustrates an exemplary cross-reference table for marked data according to an embodiment of the present disclosure.

[0011] [Figure 6] FIG. 1 illustrates a high-level block diagram of an exemplary computer system that may be used to implement one or more of the methods, tools, and modules described herein, and any associated functionality, according to embodiments of the present disclosure.

[0012] [Figure 7]1 illustrates a cloud computing environment according to an embodiment of the present disclosure.

[0013] [Figure 8] 1 illustrates abstraction model layers according to an embodiment of the present disclosure.

[0014] While the embodiments described herein are susceptible to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It is to be understood, however, that the particular embodiments described are not to be construed in a limiting sense. On the contrary, it is intended to cover all modifications, equivalents, and alternatives within the spirit and scope of the disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0015] Aspects of the present disclosure relate to the field of data storage, and more specifically to automatically generating data usage statements used to determine when data storage becomes insufficient for a respective computer program. Although the present disclosure is not necessarily limited to such applications, various aspects of the present disclosure may be understood through a discussion of various examples using this context.

[0016] Computer programs are, to some extent, a reflection of the real world. Some programs have a much longer lifespan than expected, resulting in unexpected program instability when used for current, day-to-day processing tasks. For example, as more data representing real-world entities (e.g., products, goods, monetary values, etc.) is generated and collected for various software applications, certain underlying programs may often encounter errors due to insufficient data sizes used to represent these real-world entities. For example, the array size implemented in the program code may not be large enough to handle the current data and needs to be increased. Typically, this type of code correction or maintenance work, or a combination thereof, is performed after a programmer recognizes a problem due to program instability. Furthermore, in many cases, the underlying code may be difficult to correct or prone to code errors or mistakes when programmers attempt to increase the data size for their respective programs, or both.

[0017] Embodiments of the present disclosure relate to methods, computer program products, and systems that use compiler techniques to designate when marked data requires increased data storage usage over a period of time. In embodiments, a compiler is configured to translate computer code written in one programming language into another language. For example, the compiler may translate source code and marked data (e.g., arrays or data identified as requiring increased data storage / space over a period of time) into internal code to create an executable program.

[0018] In embodiments, a compiler may identify ranges of marked data associated with source code. The ranges may specify which code may require expansion in data storage or variable storage space over a period of time. The compiler may automatically generate a data usage statement for the marked data during compilation. When each executable program is executed, usage of the marked data is generated whenever needed. In some embodiments, the compiler may generate the marked data usage statement when generating internal code. For example, the compiler may generate a first set of instructions (e.g., internal code) for taking a first snapshot of the variable storage space associated with the marked data when entering the range and a second snapshot of the variable storage space when leaving the range. The instruction set may be inserted into the executable program code during compilation. In some embodiments, the compiler may generate a second set of instructions for comparing the first and second snapshots. The second set of instructions may be inserted into the executable program code during compilation. In some embodiments, the processor may generate a third set of instructions that perform an action in response to usage of the variable storage space exceeding a threshold. The third set of instructions may be inserted into the executable program code during compilation.

[0019] In an embodiment, while the executable program is running, the instructions cause the executable program to take first and second snapshots of the variable storage space and compare data usage of the variable storage space. From the comparison, a data usage percentage is calculated. In an embodiment, an action may be performed when marked data from the data usage statement exceeds a particular predetermined limit (e.g., a data usage threshold, a data usage limit, etc.).

[0020] For example, if the marked data exceeds an 85% data storage limit for each executable program, the system may be configured to generate a notification and output the notification to the user. The notification may be in the form of a warning indicating that the data storage limit has been exceeded. The notification may be generated and sent to the user via any type of warning mechanism (e.g., through a user interface or a remote device) when the data storage limit has been exceeded.

[0021] In some embodiments, the compiler may generate a compilation list file that can be output to a user. In embodiments, the compilation list file includes cross-reference information for associated data (e.g., variables, values, etc.) of the marked data. The compilation list file may further be output to a user to assist the user in modifying their program code in response to notification that a data limit threshold has been reached. In this manner, using the cross-reference information from the compilation list file allows a programmer to expedite the code modification process by easily identifying the marked data and any associated data in the program code. Furthermore, the cross-reference information allows a programmer to reduce errors by quickly accessing related information and enabling them to carefully improve program stability.

[0022] In some embodiments, the system may utilize machine learning to analyze historical data or marked data sets, or a combination thereof, to automatically determine which programs may require modification or an increase in data storage usage. For example, based on changes or modifications to program code of historical arrays, or a combination thereof, the system may identify similar arrays that are approaching and / or likely to exceed data limits based on the data usage specification. Using this information, the system may automatically adjust the data storage of each executable program without requiring human intervention.

[0023] The above advantages are examples of advantages, and not all advantages are described. Moreover, embodiments of the present disclosure may include all, some, or none of the above advantages while remaining within the spirit and scope of the present disclosure.

[0024] Referring now to FIG. 1 , a block diagram of an exemplary system 100 according to an embodiment of the present disclosure is shown. In the illustrated embodiment, system 100 includes a client device 102 communicatively coupled to a remote device 116 via a network 150. Client device 102 and remote device 116 may be configured as any type of computer system and may be substantially similar to computer system 1101 of FIG. 6 . In an embodiment, client device 102 or remote device 116, or a combination thereof, may be a standalone computing device or a virtual software application. For example, client device 102 may be a virtual application located on a server (not shown) accessed by remote device 116 through a cloud computing network. Meanwhile, remote device 116 may be configured as a personal computer (e.g., a laptop, desktop, smartphone, etc.) operated by a user.

[0025] Network 150 may be any type of communications network, such as a wireless network or a cloud computing network. Network 150 may be substantially similar to or identical to cloud computing environment 50 illustrated in FIG. 7. In some embodiments, network 150 may be implemented using any number of any suitable communications media. For example, the network may be a wide area network (WAN), a local area network (LAN), a personal area network (PAN), the Internet, or an intranet. In particular embodiments, various systems may communicate with each other locally via any suitable local communications media.

[0026] For example, the client device 102 may communicate with the remote device 116 using a WAN, one or more hardwired connections (e.g., Ethernet cables), or a wireless communication network, or a combination thereof. In some embodiments, various systems may be communicatively coupled using one or more networks, or one or more local connections, or a combination of both. For example, in some embodiments, the client device 102 may communicate with the remote device 116 using a hardwired connection, while in some embodiments, communication between the remote device 116 and the client device 102 may be over a wireless communication network.

[0027] In the illustrated embodiment, client device 102 includes a processor 104, a compiler 106, source code and marked data 108, an executable program 110, a machine learning engine 112, and data storage 114. In some embodiments, remote device 116 may include the same, some, or similar components as client device 102.

[0028] In an embodiment, the compiler 106 is configured to convert (e.g., compile) computer code written in one programming language into another language. For example, the compiler 106 may convert the source code and marked data 108 into internal code to create the executable program 110. In an embodiment, the compiler 106 may perform multiple types of operations, such as preprocessing, lexical analysis, parsing, semantic analysis (e.g., syntax-directed conversion), conversion of the input program to an intermediate representation, code optimization, and code generation, as further described in FIG. 2 . In an embodiment, the compiler 106 may automatically generate a data usage statement from the source code and marked data 108. The data usage statement may be used to generate a notification if the data storage 114 (e.g., variable storage space) is insufficient for the executable program 110. For example, if a data storage limit is exceeded (e.g., 90% of the data storage limit), a notification may be generated and sent to a user (e.g., via a remote device 116) using the data usage statement generated by the compiler 106.

[0029] In embodiments, the machine learning engine 112 may comprise various machine learning engines (e.g., artificial neural networks, correlation engines, reinforcement feedback learning models, supervised / unsupervised learning models, etc.) configured to analyze data generated by the system 100 to improve predictions for determining modifications to the data storage 114. For example, the machine learning engine 112 may collect, monitor, or analyze, or a combination thereof, changes to past marked data or program code, or a combination thereof, to automatically determine which programs may require modifications (e.g., increases) to their data storage. For example, based on previous changes or modifications, or a combination thereof, to program code of historical arrays, the system may identify similar arrays that are likely to approach or exceed data limits based on the data usage specification. Using this historical data, the system 100 may automatically predict, adjust, or a combination thereof, data storage for each executable program before various limits are exceeded, without requiring human intervention.

[0030] Machine learning algorithms may include, but are not limited to, decision tree learning, association rule learning, artificial neural networks, deep learning, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity / distance training, sparse dictionary learning, genetic algorithms, rule-based learning, or other machine learning techniques, or combinations thereof.

[0031] For example, machine learning algorithms may include the following exemplary techniques: K-nearest neighbors (KNN), learning vector quantization (LVQ), self-organizing maps (SOM), logistic regression, ordinary least squares regression (OLSR), linear regression, stepwise regression, multivariate adaptive regression splines (MARS), ridge regression, least absolute shrinkage and selection operator (LASSO), elastic net, least angle regression (LARS), probabilistic classifiers, naive Bayes classifiers, binary classifiers, linear classifiers, hierarchical classifiers, positive and negative classifiers, and positive and negative classifiers. Correlation analysis (CCA), factor analysis, independent component analysis (ICA), linear discriminant analysis (LDA), multidimensional scaling (MDS), non-negative matrix factorization (NMF), partial least squares regression (PLSR), principal component analysis (PCA), principal component regression (PCR), Sammon mapping, t-distributed stochastic neighbor embedding (t-SNE), bootstrap aggregating, harmonic mean, gradient boosting decision tree (GBDT), gradient boosting machine (GBM), inductive bias algorithm, Q Learning may utilize one or more of the following machine learning techniques, or a combination thereof: state-action-reward-state-action (SARSA), temporal difference (TD) learning, a priori algorithm, equivalent class transformation (ECLAT) algorithm, Gaussian process regression, gene expression programming, group methods of data processing (GMDH), inductive logic programming, learning by example, logic model trees, information fuzzy networks (IFN), hidden Markov models, Gaussian naive Bayes, multinomial naive Bayes, average-one dependence estimator (AODE), Bayesian networks (BN), classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), expectation maximization algorithm, forward propagation neural networks, logic learning machines, self-organizing maps, single-link clustering, fuzzy clustering, hierarchical clustering, Boltzmann machines, convolutional neural networks, recurrent neural networks, hierarchical temporary memories (HTM), or other machine learning techniques.

[0032] Figure 1 is intended to illustrate representative major components of system 100. However, in some embodiments, individual components may be of greater or less complexity than depicted in Figure 1, and components other than or in addition to those shown in Figure 1 may be present, and the number, type, and configuration of such components may vary. Similarly, one or more components shown with system 100 may not be present, and the arrangement of components may differ.

[0033] For example, Figure 1 illustrates an exemplary system 100 having a single client device 102 and a single remote device 116 communicatively coupled via a single network 150. However, a suitable network architecture for implementing embodiments of the present disclosure may include any number of client devices, remote devices, and networks. The various models, modules, systems, and components illustrated in Figure 1 may exist across multiple client devices, remote devices, and networks, if any.

[0034] Referring now to Figure 2, a block diagram of an exemplary compiler 106 according to an embodiment of the present disclosure is shown. Compiler 106 may be the same as or similar to compiler 106 in Figure 1. In the illustrated embodiment, compiler 106 comprises a front-end layer 200 and a back-end layer 202. In an embodiment, front-end layer 200 comprises lexical analyzer 204, parser 206, internal code generator 208, marked-data dictionary information 210, dictionary information 212, and list 214. Back-end layer 202 comprises optimizer 218 and code generator 220.

[0035] In an embodiment, lexical analyzer 204 is configured to perform lexical analysis on the received source code and marked data 108. Once lexical analysis is performed, parser 206 performs syntactic analysis to determine whether the provided input is in the correct syntax of the programming language. During the lexical analysis and syntactic analysis phase, compiler 106 generates dictionary information 212 containing the marked data and associated data and marked data dictionary information 210. Dictionary information 212 and marked data dictionary information 210 may include cross-reference information shared between the marked data and associated data (e.g., variables, values, etc.).

[0036] In embodiments, during the internal code generation phase, the internal code generator 208 generates data usage details to calculate marked data usage status. In embodiments, the internal code generator 208 inserts the data usage details into the executable program code. For example, the internal code generator 208 may generate a first set of instructions for taking a first snapshot of variable storage space associated with the marked data when entering a range of the marked data and a second snapshot of the variable storage space when leaving the range. The internal code generator 208 may insert the first set of instructions into the executable program code during compilation. In embodiments, the internal code generator 208 may generate a second set of instructions for comparing the first and second snapshots. The internal code generator 208 may insert the second set of instructions into the executable program code during compilation. In some embodiments, the internal code generator 208 may insert a third set of instructions into the executable program code that performs an action in response to exceeding a data threshold (e.g., a data usage threshold).

[0037] In this manner, when executable program 110 enters a range of marked data, executable program 110 is configured to save a first snapshot of the variable storage space of data storage 114. When exiting a range of marked data, executable program 110 saves a second snapshot of the variable storage space of data storage 114. The first and second snapshots of the variable storage space are compared by executable program 110, and a data usage percentage is calculated. In embodiments, when the marked data exceeds a particular limit (e.g., a data usage threshold, a data usage limit, etc.), executable program 110 may generate and output a notification or warning to a user.

[0038] In an embodiment, during the compilation list generation phase, compiler 106 generates a list 214 comprising cross-reference information for the marked data and associated data determined from dictionary information 210 and dictionary information 212. In an embodiment, compiler 106 may use list 214 to generate compilation list file 216 that may be output to a user. When a notification is sent to a user indicating that variable storage space in data storage 114 is insufficient for executable program 110, the cross-reference information in compilation list file 216 may be used to identify which program code in the executable program requires modification. The compilation list file 216 may include one or more of the following: cross-reference information for the marked data, cross-reference information for aliases of the marked data (e.g., via a compiler aliasing mechanism), cross-reference information for left value data when the marked data of an assignment statement is a right value, cross-reference information for left value data when the size of the marked data of an assignment statement is a right value, and cross-reference information for another operand data when the size of the marked data of a conditional statement (e.g., a loop termination decision statement) is an operand. In this way, the compilation file 216 can be utilized by a user (e.g., a programmer) to easily track where to modify program code related to notifications of insufficient data space.

[0039] In an embodiment, optimizer 218 may receive the internal code generated by internal code generator 208 and the data usage specification. Optimizer 218 is configured to utilize the data usage specification to modify the received internal code to improve the operation of executable program 110. For example, optimizer 218 may analyze the data usage specification and modify the internal code to cause the executable program to operate using less memory. Code generator 220 then converts the modified code into a form of program code that may be easily executed / readable by executable program 110.

[0040] Referring now to Figure 3, a flow diagram of an exemplary process 300 for automatically generating a data usage statement and notifying a user when a data usage threshold is exceeded is shown, according to an embodiment of the present disclosure. Process 300 may be performed by processing logic comprising hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executing on a processor), firmware, or a combination thereof. In some embodiments, process 300 is a computer-implemented process. Process 300 may be performed by processor 104 illustrated in Figure 1.

[0041] Process 300 begins by receiving a set of marked data associated with a set of source code. In an embodiment, the set of marked data associated with the set of source code may be received by a compiler (e.g., compiler 106 of FIGS. 1 and 2). This is shown in step 305.

[0042] Process 300 continues by identifying a range of source code associated with marked data. This is shown at step 310. The range of a set of marked data may specify which source code may require expansion in data storage or variable storage space over a period of time. Examples of ranges of source code associated with marked data are further described in FIG. 4.

[0043] Process 300 continues by generating a first set of instructions for taking a first snapshot of the variable storage space associated with the marked data when entering a range and a second snapshot of the variable storage space when leaving a range. This is shown at step 315. In an embodiment, the compiler generates the marked data usage specification when generating the internal code. The compiler may insert the first set of instructions into the executable program code during compilation such that the executable program is configured to take snapshots of the variable storage space when entering and leaving a range associated with the marked data. An example of entering a range of marked data is further described in FIG. 4.

[0044] Process 300 may continue by generating a second set of instructions for comparing the first and second snapshots to determine variable storage space usage associated with the marked data. This is shown at step 320. In an embodiment, a compiler generates the second set of instructions for comparing the first and second snapshots and inserts the second set of instructions into the executable program code during compilation. In an embodiment, while the executable program is running, the instructions cause the executable program to compare the snapshots to determine data usage of the variable storage space. In an embodiment, the executable program continuously compares the snapshots each time the snapshots enter or leave a range until it is determined that data usage of the variable storage space has exceeded a threshold. In an embodiment, the second set of instructions further causes the program to compare the determined data usage with a threshold.

[0045] Process 300 continues by generating a third set of instructions to perform an action in response to the variable storage space usage exceeding a threshold. This is shown at step 325. In an embodiment, a compiler generates the third set of instructions to perform the action and inserts the third set of instructions into the executable program code during compilation. In an embodiment, the executable program may perform an action in response to exceeding a data threshold (e.g., a data usage threshold). For example, when the variable storage space threshold is exceeded, a notification may be generated and sent to a user. In an embodiment, the notification may be a warning indicating that the set of marked data has exceeded a data usage limit (e.g., a 75% data usage limit).

[0046] Process 300 continues by compiling the source code into an executable file, where the executable file includes the first, second, and third instruction sets, as shown in step 330. In an embodiment, a compiler may insert instructions into the executable program code.

[0047] In embodiments, an application or program may use executable program code to automatically determine when data storage becomes insufficient for the respective computer program. For example, the application may take a first snapshot of the variable storage space associated with a marked object when the application enters a range of marked data during execution of the application. The application may take a second snapshot of the variable storage space when the application exits a range of marked data. The application may compare the first and second snapshots to determine usage of the variable storage space associated with the marked data. The application may compare the usage to a threshold usage. When the threshold usage is exceeded, the application may perform an action (e.g., output a warning or notification) in response to the usage exceeding the threshold usage.

[0048] In some embodiments, the process may further include determining dictionary information for associated data of the set of marked data. The compiler may generate a compilation list file comprising the dictionary information. Once the compilation list file is generated, it may be output to the user along with notifications to assist the user in determining which program code requires modification to increase available data usage space.

[0049] In embodiments, the dictionary information of the compilation list file may include various cross-reference information. Examples of cross-reference information are described in further detail in FIG. 5. For example, the cross-reference information may relate to a set of marked data and an alias for the set of marked data. In some embodiments, the cross-reference information may include information on left-value data when the set of marked data in an assignment statement is a right-value. In some embodiments, the cross-reference information may include information on left-value data when the data size of the set of marked data in an assignment statement is a right-value. In some embodiments, the cross-reference information may include information on another operand data when the data size of the set of marked data in a conditional statement is an operand. These are merely examples of cross-reference information and are not meant to be limiting.

[0050] In some embodiments, the system may utilize machine learning to analyze historical data or marked data sets, or a combination thereof, to automatically determine which programs may require modification or an increase in data storage usage, or a combination thereof. In such examples, a notification may or may not need to be output to the user. For example, based on changes or modifications to program code of historical arrays, or a combination thereof, the system may identify similar arrays that are approaching and / or likely to exceed data limits based on the data usage specification. Using this information, the system may automatically adjust the data storage of each executable program without requiring human intervention. In this way, because the data storage has been automatically expanded, the system may not need to send a warning or notification to the user. However, in some embodiments, the system may still send a notification to inform the user that the data storage has been expanded.

[0051] Referring to FIG. 4, an exemplary set of marked data and program code 400 according to an embodiment of the present disclosure is shown. During the internal code generation phase, the compiler generates a data usage statement for calculating the marked data usage status. In the illustrated embodiment, "emp_table" is identified as a set of marked data. The compiler may identify a range of marked data by determining a range entry point 402 and an exit point 404. Once determined, the compiler may insert instructions into the executable program code during compilation such that a first snapshot of the storage space of emp_table is saved when the executable program enters the range entry point 402 of the set of marked data. The instructions may further include executable program code that causes the executable program to save a second snapshot of the storage space of emp_table when the range exits the range exit point 404. The instructions may further include executable program code that causes the executable program to compare the first and second snapshots, calculate a data usage percentage, and generate a data usage statement. The instructions further include executable program code that causes the executable program to generate and output a notification that the marked data has exceeded a data usage limit. For example, when the marked data (e.g., "emp_table") exceeds a certain predetermined limit or threshold (e.g., 80% data space usage), the executable program generates and outputs a notification to a user. For example, the notification may be configured as a warning message stating, "emp_table usage has exceeded 80%."

[0052] Referring now to FIG. 5, an exemplary cross-reference table 500 for marked data according to an embodiment of the present disclosure is shown. In the illustrated embodiment, the marked data is identified as "emp_table" as illustrated in FIG. 4. In an embodiment, a compilation list file may be generated and output along with the compiler notification. The compilation list file may contain various cross-reference information associated with the set of marked data that may be used by a programmer for quick reference when performing modifications to any respective program code (e.g., during data storage expansion programming).

[0053] For example, in the illustrated embodiment, the compilation list file includes a cross-reference table 500 that identifies cross-reference information (values) associated with a set of marked data 502 (e.g., "emp_table"). In another example, the cross-reference table 500 may include cross-reference information for left value data when the set of marked data of an assignment statement is a right value 504 (e.g., "emp_sort_table"). In another example, the cross-reference table 500 may include cross-reference information associated with an alias (via a compiler aliasing mechanism) for a set of marked data 506 (e.g., "emp_current"). In another example, the cross-reference table 500 may include cross-reference information for another operand data when the data size of the set of marked data of a conditional statement is an operand 508 (e.g., "MAX_HEADCOUNT"). In another example, the cross-reference table 500 may include cross-reference information for left value data when the data size of the set of marked data of an assignment statement is a right value 510 (e.g., "i"). Using the compilation list file, a user (e.g., a programmer) can reduce programming time by using cross-reference information and quickly decipher where data usage exceeds a particular limit. In this way, a user can reduce timely errors associated with programming mistakes and increase the stability of their respective programs by detecting insufficient data space before the program reaches its storage limit.

[0054] 6, a high-level block diagram of an exemplary computer system 1101 that may be used to implement one or more of the methods, tools, and modules, and any associated functionality, described herein (e.g., using one or more processor circuits of a computer or computer processor) is shown in accordance with an embodiment of the present disclosure. In some embodiments, the major components of computer system 1101 may include one or more CPUs 1102, a memory subsystem 1104, a terminal interface 1112, a storage interface 1116, an I / O (input / output) device interface 1114, and a network interface 1118, all of which may be communicatively coupled, directly or indirectly, via a memory bus 1103, an I / O bus 1108, and an I / O bus interface 1110, for inter-component communication.

[0055] Computer system 1101 may include one or more general-purpose programmable central processing units (CPUs) 1102A, 1102B, 1102C, and 1102D, collectively referred to herein as CPUs 1102. In some embodiments, computer system 1101 may include multiple processors, as is typical for relatively large systems. However, in other embodiments, computer system 1101 may alternatively be a single CPU system. Each CPU 1102 may execute instructions stored in memory subsystem 1104, which may include one or more levels of on-board cache. In some embodiments, a processor may include at least one or more memory controllers or storage controllers, or a combination thereof. In some embodiments, a CPU may execute processes included herein (e.g., processes 200, 300, and 400).

[0056] System memory subsystem 1104 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 1122 or cache memory 1124. Computer system 1101 may also include other removable / non-removable, volatile / non-volatile computer system data storage media. By way of example only, storage system 1126 may be provided to read from and write to non-removable, non-volatile magnetic media, such as a “hard drive.” Although not shown, a magnetic disk drive may be provided to read from or write to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), or an optical disk drive may be provided to read from and write to a removable, non-volatile optical disk, such as a CD-ROM, DVD-ROM, or other optical media. Additionally, memory subsystem 1104 may include flash memory, such as a flash memory stick drive or flash drive. Memory devices may be connected to memory bus 1103 by one or more data media interfaces. The memory subsystem 1104 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments.

[0057] 6 as a single bus structure providing a direct communication path between CPU 1102, memory subsystem 1104, and I / O bus interface 1110, memory bus 1103, in some embodiments, may include multiple different buses or communication paths, which may be arranged in any of a variety of configurations, such as point-to-point links in a hierarchical, star, or web configuration, multiple hierarchical buses, parallel and redundant paths, or any other suitable type of configuration. Furthermore, while I / O bus interface 1110 and I / O bus 1108 are shown as a single unit, computer system 1101, in some embodiments, may include multiple I / O bus interfaces 1110, multiple I / O buses 1108, or both. Furthermore, while multiple I / O interface units are shown separating I / O bus 1108 from the various communication paths to the various I / O devices, in other embodiments, some or all of the I / O devices may be directly connected to one or more system I / O buses.

[0058] In some embodiments, computer system 1101 may be a multi-user mainframe computer system, a single-user system, or a server computer or similar device that has little or no direct user interface but receives requests from other computer systems (clients). Further, in some embodiments, computer system 1101 may be implemented as a desktop computer, a portable computer, a laptop or notebook computer, a tablet computer, a pocket computer, a telephone, a smartphone, a network switch or router, or any other suitable type of electronic device.

[0059] It should be noted that Figure 6 is intended to illustrate representative major components of exemplary computer system 1101. However, in some embodiments, individual components may have greater or less complexity than depicted in Figure 6, components other than or in addition to those shown in Figure 6 may be present, and the number, type, and configuration of such components may vary.

[0060] One or more programs / utilities 1128, each having a set of at least one program module 1130, may be stored in the memory subsystem 1104. The programs / utilities 1128 may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data, or some combination thereof, may comprise an implementation of a network environment. The programs / utilities 1128 or program modules 1130, or a combination thereof, generally perform the functions or methodologies of various embodiments.

[0061] Although this disclosure includes a detailed description of cloud computing, it should be understood in advance that implementation of the teachings described herein is not limited to cloud computing environments. Rather, embodiments of the present disclosure can be implemented in connection with any other type of computing environment now known or later developed.

[0062] Cloud computing is a service delivery model that enables convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a service provider. This cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0063] The characteristics are as follows:

[0064] On-Demand Self-Service: Cloud consumers can unilaterally provision computing capacity, such as server time and network storage, automatically as needed, without requiring human interaction with the provider of the service.

[0065] Wide network access: Capabilities are available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin or thick client platforms (eg, cell phones, laptops, and PDAs).

[0066] Resource Pooling: Computing resources of a provider are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources being dynamically allocated and reallocated on demand. There is an implication of location independence in that consumers generally have no control or knowledge over the exact location of the resources provided, although location may be identifiable at a higher level of abstraction (e.g., country, state, or data center).

[0067] Rapid Elasticity: Capacity can be quickly and elastically provisioned, in some cases automatically, to quickly scale out, and quickly released to quickly scale in. To the consumer, the processing power available for provisioning often appears unlimited, and can be purchased in any amount at any time.

[0068] Metered Services: Cloud systems automatically control and optimize resource usage using metering capabilities at a level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported to provide transparency to both providers and consumers of the services used.

[0069] The service model is as follows:

[0070] Software as a Service (SaaS): The consumer is offered the ability to use a provider's applications running on a cloud infrastructure. The applications are accessible from a variety of client devices through a thin-client interface such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

[0071] Platform as a Service (PaaS): The ability offered to consumers is to deploy consumer-created or acquired applications, written using programming languages ​​and tools supported by the provider, on a cloud infrastructure. The consumer does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but does have control over the deployed applications and, in some cases, application hosting environment configuration.

[0072] Infrastructure as a Service (IaaS): The ability offered to consumers is to provision processing, storage, network, and other underlying computing resources. The consumer can deploy and run any software, which may include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure, but does have control over the operating system, storage, deployed applications, and in some cases, limited control of selected network components (e.g., host firewalls).

[0073] The deployment model is as follows:

[0074] Private Cloud: Cloud infrastructure is run exclusively for an organization. It can be managed by that organization or a third party and can exist on-premise or off-premise.

[0075] Community Cloud: Cloud infrastructure is shared by multiple organizations to support a specific community of shared interests (e.g., mission, security requirements, policies, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.

[0076] Public Cloud: Cloud infrastructure is made available to the general public or large industry groups and is owned by organizations that sell cloud services.

[0077] Hybrid Cloud: A cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain distinct entities but are tied together by standardized or proprietary technologies that allow for data and application portability (e.g., cloud bursting to balance load between clouds).

[0078] Cloud computing environments are service-oriented and focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that comprises a network of interconnected nodes.

[0079] Referring now to FIG. 7, an exemplary cloud computing environment 50 is illustrated. As shown, the cloud computing environment 50 includes one or more cloud computing nodes 10 with which local computing devices used by cloud consumers (e.g., a personal digital assistant (PDA) or mobile phone 54A, a desktop computer 54B, a laptop computer 54C, or an automotive computer system 54N, or combinations thereof) can communicate. The nodes 10 may communicate with each other. They may be physically or virtually grouped (not shown) in one or more networks, or combinations thereof, such as private, community, public, or hybrid clouds as described above. This allows the cloud computing environment 50 to provide infrastructure, platform, or software, or combinations thereof, as a service without requiring cloud consumers to maintain resources on local computing devices. The types of computing devices 54A-N illustrated in FIG. 7 are intended to be merely exemplary, and it will be understood that the computing nodes 10 and the cloud computing environment 50 can communicate with any type of computerized device over any type of network or network-addressable connection, or combinations thereof (e.g., using a web browser).

[0080] Referring now to Figure 8, a set of functional abstraction layers provided by cloud computing environment 50 (Figure 7) is shown. It should be understood in advance that the components, layers, and functions shown in Figure 8 are intended to be merely exemplary, and embodiments of the present invention are not limited thereto. As shown, the following layers and corresponding functions are provided:

[0081] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include a mainframe 61, a RISC (reduced instruction set computer) architecture-based server 62, a server 63, a blade server 64, a storage device 65, and a network and network components 66. In some embodiments, the software components include network application server software 67 and robotic device scheduling software 68, associated with the client device 102 of FIG. 1 .

[0082] The virtualization layer 70 provides an abstraction layer from which the following example virtual entities can be provided: virtual servers 71, virtual storage 72, virtual networks including virtual private networks 73, virtual applications and operating systems 74, and virtual clients 75.

[0083] In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing and other resources utilized to execute tasks within the cloud computing environment. Metering and pricing 82 provides cost tracking as resources are utilized within the cloud computing environment and provides charging or billing for the consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification of cloud consumers and protection for tasks, as well as data and other resources. User portal 83 provides consumers and system administrators with access to the cloud computing environment. Service level management 84 provides allocation and management of cloud computing resources so that required service levels are met. Service level agreement (SLA) planning and fulfillment 85 provides pre-provisioning and procurement of cloud computing resources that anticipate future requirements according to SLAs.

[0084] Workload tier 90 provides examples of functions for which a cloud computing environment may be utilized. Examples of workloads and functions that may be provided from this tier include mapping and navigation 91, software development and lifecycle management 92, virtual classroom instruction delivery 93, data analytics processing 94, transaction processing 95, and mobile desktop 96.

[0085] As described in more detail herein, it is contemplated that some or all of the operations of some of the method embodiment described herein may be performed in an alternative order, or not performed at all. Furthermore, multiple operations may occur simultaneously or within a larger process.

[0086] The present invention may be a system, method, or computer program product, or combination thereof, at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of the present invention.

[0087] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or ridge structures in grooves having instructions recorded thereon, and any suitable combination of the foregoing. Computer-readable storage medium, as used herein, is not to be construed as a transitory signal per se, such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted over a wire.

[0088] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, or a wireless network, or both. The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers, or combinations thereof. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage on a computer-readable storage medium within the respective computing / processing device.

[0089] The computer-readable program instructions for carrying out the operations of the present invention may be either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk® or C++, and procedural programming languages ​​such as the “C” programming language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be to an external computer (e.g., through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry to perform aspects of the present invention.

[0090] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0091] These computer-readable program instructions may be provided to a computer processor or other programmable data processing apparatus to produce a machine, such that the instructions, when executed by the computer processor or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus or other device, or combination thereof, to function in a particular manner, such that the computer-readable storage medium on which the instructions are stored comprises an article of manufacture containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0092] The computer-readable program instructions may be loaded into a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable apparatus, or other device to perform a series of operational steps to create a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device implement the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0093] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may actually be implemented as a single step, or may be executed simultaneously, substantially simultaneously, partially, or fully overlapping in time, or the blocks may even be executed in the reverse order depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by a dedicated hardware-based system that performs the specified functions or operations or executes a combination of dedicated hardware and computer instructions.

[0094] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of various embodiments. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It is to be further understood that the terms "includes" and / or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, or components, or combinations thereof, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups or combinations thereof. In the preceding detailed description of exemplary embodiments of various embodiments, reference has been made to the accompanying drawings, in which like numerals represent like elements. The accompanying drawings form a part hereof, and in which are shown, by way of illustration, specific exemplary embodiments in which various embodiments may be practiced. Although these embodiments have been described in sufficient detail to enable those skilled in the art to practice the embodiments, other embodiments may be used, and logical, mechanical, electrical, and other changes may be made without departing from the scope of the various embodiments. In the foregoing description, numerous specific details are set forth to provide a thorough understanding of the various embodiments. However, various embodiments may be practiced without these specific details. In other instances, well-known circuits, structures, and techniques have not been shown in detail so as not to obscure the embodiments.

[0095] As used herein, "a number of," when used in reference to an item, means one or more of the item. For example, "several different types of networks" refers to one or more different types of networks.

[0096] Where different reference numerals have a common number followed by a different letter (e.g., 100a, 100b, 100c) or a common number followed by a punctuation mark followed by a different number (e.g., 100-1, 100-2 or 100.1, 100.2), use of the reference numeral alone, without a letter or subsequent number (e.g., 100), may refer to the group of elements, any subset of the group, or exemplary representatives of the group as a whole.

[0097] Furthermore, the phrase "at least one," when used in conjunction with a list of items, means that different combinations of one or more of the listed items may be used, and only one of each item in the list may be required. In other words, "at least one" means that any combination of items and many items from the list may be used, but not all items in the list are required. An item may be a particular object, thing, or category.

[0098] For example, without limitation, "at least one of item A, item B, or item C" may include item A, item A and item B, or item B. This example may also include item A, item B and item C, or item B and item C. Of course, any combination of these items may be present. In some illustrative examples, "at least one" may be, without limitation, for example, two of item A, one of item B, ten of item C, four of item B and seven of item C, or other suitable combinations.

[0099] Different instances of the word "embodiment" as used herein do not necessarily refer to the same embodiment, but may refer to the same embodiment. Any data and data structures shown or described herein are merely examples; in other embodiments, different amounts of data, types of data, fields, number and types of fields, field names, number and types of columns, records, entries, or organizations of data may be used. Furthermore, any data may be combined with logic such that separate data structures are not required. Therefore, the foregoing detailed description should not be taken in a limiting sense.

[0100] The descriptions of various embodiments of the present disclosure have been presented for illustrative purposes, but are not intended to be exhaustive or limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein have been selected to best explain the principles of the embodiments, practical applications, or technical improvements over technologies found in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0101] While the present invention has been described in terms of specific embodiments, it is anticipated that variations and modifications thereof will become apparent to those skilled in the art. It is therefore intended that the following claims be interpreted to cover all such variations and modifications as fall within the true spirit and scope of the invention.

Claims

1. A method comprising: receiving, by a computer, a set of marked data associated with a set of source code; the computer identifying a range of the source code associated with the marked data; generating a first set of instructions for the computer to take a first snapshot of a variable storage space associated with the marked data when the range is entered and a second snapshot of the variable storage space when the range is exited; generating a second set of instructions by the computer to compare the first snapshot and the second snapshot to determine the variable storage space usage associated with the marked data; generating, by the computer, a third set of instructions for performing an action in response to the usage of the variable storage space exceeding a threshold; the computer compiling the source code into an executable file, the executable file including the first instruction set, the second instruction set, and the third instruction set; A computer-implemented method comprising:

2. The method of claim 1, further comprising: determining dictionary information for associated data in the set of marked data; generating a compilation list file comprising the dictionary information by the computer; The computer-implemented method of claim 1 further comprising:

3. The dictionary information of the compilation list file is cross-reference information relating to said set of marked data; cross-reference information relating to aliases of said set of marked data; cross-reference information of left value data when the set of marked data of an assignment statement is a right value; cross-reference information of the left value data when the data size of the set of marked data of the assignment statement is the right value; Cross-reference information of another operand data when the data size of the set of marked data of the conditional statement is an operand; The computer-implemented method of claim 2 , comprising at least one selected from the group consisting of:

4. A computer-implemented method as described in claim 2 or 3, further comprising a step in which the computer outputs the compilation list file to a user.

5. The computer-implemented method of claim 1 , wherein the action responsive to the usage of the variable storage space exceeding a threshold comprises instructions for outputting a notification to a user.

6. The computer-implemented method of claim 5 , wherein the notification indicates that the set of marked data has exceeded a data usage limit.

7. A computer-implemented method as described in claim 5 or 6, further comprising a step in which the computer, in response to the notification, receives input from the user to modify the variable storage space.

8. The computer-implemented method of claim 7 , wherein the input from the user augments the variable data storage.

9. a processor; communicatively coupled to the processor and when executed by the processor, receiving a set of marked data associated with a set of source code; identifying a range of the source code associated with the marked data; generating a first set of instructions for taking a first snapshot of a variable storage space associated with the marked data when the range is entered and a second snapshot of the variable storage space when the range is exited; generating a second set of instructions to compare the first snapshot and the second snapshot to determine the variable storage space usage associated with the marked data; generating a third set of instructions to perform an action in response to the usage of the variable storage space exceeding a threshold; compiling the source code into an executable file, the executable file including the first instruction set, the second instruction set, and the third instruction set; a computer-readable storage medium storing program instructions that cause the processor to perform a method comprising: A system comprising:

10. The method executed by the processor comprises: determining dictionary information for associated data of the set of marked data; generating a compilation list file comprising the dictionary information; The system of claim 9 further comprising:

11. The dictionary information of the compilation list file is cross-reference information relating to said set of marked data; cross-reference information relating to aliases of said set of marked data; cross-reference information of left value data when the set of marked data of an assignment statement is a right value; cross-reference information of the left value data when the data size of the set of marked data of the assignment statement is the right value; Cross-reference information of another operand data when the data size of the set of marked data of the conditional statement is an operand; 11. The system of claim 10, comprising at least one selected from the group consisting of:

12. 12. The system of claim 9, wherein the action responsive to the usage of the variable storage space exceeding a threshold comprises instructions for outputting a notification to a user.

13. The system of claim 12 , wherein the notification indicates that the set of marked data has exceeded a data usage limit.

14. The system of claim 12 or 13, further comprising receiving input from the user to modify the variable storage space in response to the notification.

15. A computer program having program instructions embodied therein, the program instructions causing a processor to: receiving a set of marked data associated with a set of source code; identifying a range of the source code associated with the marked data; generating a first set of instructions for taking a first snapshot of a variable storage space associated with the marked data when the range is entered and a second snapshot of the variable storage space when the range is exited; generating a second set of instructions to compare the first snapshot and the second snapshot to determine the variable storage space usage associated with the marked data; generating a third set of instructions to perform an action in response to the usage of the variable storage space exceeding a threshold; compiling the source code into an executable file, the executable file including the first instruction set, the second instruction set, and the third instruction set; A computer program executable by the processor to cause the processor to perform a method comprising:

16. The method executed by the processor comprises: determining dictionary information for associated data of the set of marked data; generating a compilation list file comprising the dictionary information; The computer program of claim 15 further comprising:

17. The dictionary information of the compilation list file is cross-reference information relating to said set of marked data; cross-reference information relating to aliases of said set of marked data; cross-reference information of left value data when the set of marked data of an assignment statement is a right value; cross-reference information of the left value data when the data size of the set of marked data of the assignment statement is the right value; Cross-reference information of another operand data when the data size of the set of marked data of the conditional statement is an operand; 17. The computer program of claim 16, comprising at least one selected from the group consisting of:

18. 18. The computer program product of claim 16 or 17, wherein the method executed by the processor further comprises outputting the compilation list file to a user.

19. 19. The computer program product of claim 15, wherein the action responsive to the usage of the variable storage space exceeding a threshold comprises instructions for outputting a notification to a user.

20. The method executed by the processor further comprises:

20. The computer program product of claim 19, comprising receiving input from the user to increase the variable storage space in response to the notification.

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