Computer device for AI-based prediction of software errors

Abstract

A computer device for predicting software errors based on artificial intelligence, wherein the device comprises the following: at least one processing unit that is operationally coupled with a non-volatile storage unit; a data acquisition unit configured to receive software development artifacts such as source code files, change histories, execution logs, compilation results, and error notes from one or more external computer systems via a communication interface; a preprocessing processor configured to normalize received software development artifacts and structurally transform them into standardized data representations by performing token separation, structural parsing, and metric alignment operations; a feature extraction processor that is operationally connected to the preprocessing processor and is configured to generate multidimensional feature representations that correspond to software complexity attributes, dependency relationships, code change frequency, and execution behavior patterns; a learning processor that is stored in non-volatile memory and executable by the processing unit, wherein the learning processor is configured to create predictive models by iterative parameter adjustment using historical feature representations and corresponding error indicators; a prediction processor configured to apply prediction models to newly generated feature representations to determine a probability value associated with the occurrence of software errors in identified software components; a feedback processing unit configured to receive confirmed error information and update the predictive models by integrating newly acquired error results into stored training data; and an output interface unit configured to output prediction results, indicating the probability of errors, affected software components, and severity estimates, to an external computing environment.

Description

Technical field

[0001] The present disclosure relates generally to the field of intelligent computer systems and in particular to a specialized computer device configured for artificial intelligence-based prediction of software errors through structured data acquisition, feature processing and predictive modeling within a dedicated machine architecture. background

[0002] Modern software systems are characterized by increasing complexity, distributed development environments, and frequent version updates, which together increase the likelihood of errors during the development and deployment phases. Traditional testing methods such as manual inspection and rule-based validation are often insufficient to detect latent errors, logical inconsistencies, and performance anomalies early on. Existing automated tools are primarily based on static analysis or historical heuristics and lack adaptive learning capabilities. Therefore, there is a need for a system that processes software artifacts using AI techniques to proactively predict potential errors. This enables early intervention, improves code quality, and reduces maintenance costs.

[0003] Software systems have become increasingly complex due to the widespread use of large-scale, distributed architectures, microservices-based development, continuous integration and continuous deployment, and rapidly changing user requirements. As the size and complexity of software grows, so does the likelihood of errors occurring during development, integration, or maintenance. Software errors, which can include logical flaws, performance bottlenecks, security vulnerabilities, and functional inconsistencies, can lead to system failures, financial losses, and a decline in user confidence. Therefore, the early detection of potential errors has become a central concern in software development.Traditional methods for error detection rely heavily on manual code reviews, structured test protocols, and developer experience, often resulting in delayed problem detection and limited scalability in large codebases. As development environments become increasingly dynamic and collaborative, the early and reliable prediction of software errors before deployment has become an essential capability.

[0004] Existing solutions for detecting software errors largely rely on static code analysis tools that examine source code for syntactic inconsistencies, violations of programming guidelines, and known error patterns. These tools analyze the code and apply predefined rules to identify potential problems. While such approaches can detect certain types of errors, they are limited by the scope of their rule sets and often fail to capture complex logical errors or context-specific errors that occur at runtime. Static analysis tools typically generate a large number of alerts, many of which can be false positives. This increases the overhead for developers to manually review each flagged instance.Furthermore, rule-based analysis does not effectively adapt to evolving programming paradigms or project-specific coding practices, making it difficult to maintain accuracy over time.

[0005] Dynamic testing solutions represent another widely used approach, where software is run under various conditions to observe errors, performance limitations, or unexpected behavior. Unit tests, integration tests, and system tests are standard procedures in modern development environments. While these methods can uncover errors that occur during execution, they rely on test coverage, which is often incomplete due to time and resource constraints. Many errors go undetected because they occur under rare conditions not covered by predefined test cases. Furthermore, dynamic testing is typically performed in later stages of development, meaning that errors are often discovered only after significant time and effort have already been invested in coding and integration, making fixes more expensive and complex.

[0006] Another possible solution is the use of software metrics-based error prediction models. These approaches rely on the statistical analysis of code metrics such as lines of code, code complexity, coupling, cohesion, and change frequency to estimate the probability of errors. Early research in this area used traditional machine learning methods trained on historical project data to classify software modules as error-prone or error-free. While these models took a more data-driven approach, they have some limitations. The effectiveness of such models depends heavily on the quality and completeness of the historical error data, which is not always available or consistent across projects.Furthermore, software projects differ considerably in terms of architecture, programming practices, and team dynamics, which limits the generalizability of models trained on one dataset when applied to another dataset.

[0007] Version control and change history analysis tools are also used to identify high-risk areas of code based on patterns such as frequent changes, large code commits, or repeated bug fixes. These systems attempt to determine vulnerability by examining how often certain components are modified over time. While such approaches provide valuable insights into software development, they do not directly analyze the internal structure or logic of the code and can therefore miss errors that arise in less frequently modified modules. Furthermore, change-based methods can lead to misleading predictions if changes are driven by feature enhancements rather than bug fixes.

[0008] In recent years, artificial intelligence and machine learning have been explored for improving error prediction. Some existing AI-based solutions utilize neural networks, support vector machines, or ensemble learning models trained on software metrics and error datasets. These approaches offer higher predictive accuracy compared to rule-based systems because they can learn patterns and relationships from the data. However, many existing AI-based solutions are purely software frameworks that run on standard computer platforms without dedicated architectural support. This often results in high computational overhead, slow processing of large software datasets, and limited real-time predictive capabilities. Furthermore, training and inference processes can require significant computational resources, making them difficult to deploy in continuous monitoring environments.

[0009] Another drawback of current AI-based approaches is the lack of integration between data acquisition, preprocessing, feature extraction, model training, and prediction. In many existing systems, these components operate as separate modules connected via external tools or scripts. This fragmented architecture leads to inefficiencies, delays in data flow, and an increased risk of data inconsistencies. Furthermore, synchronizing multiple software tools can be challenging, especially in fast-paced development environments with frequent code changes.

[0010] Many existing error prediction systems rely primarily on structured numerical metrics and fail to effectively integrate unstructured data sources such as code comments, execution logs, developer notes, or bug report descriptions. These unstructured sources contain valuable contextual information that could improve prediction accuracy but are often difficult to process using conventional analysis methods. Therefore, current solutions may miss the subtle indicators of errors hidden within textual or behavioral data.

[0011] Scalability remains a major limitation of current technologies. As software repositories and development teams grow, so does the volume of data generated during development. Processing large datasets with traditional computer architectures can lead to increased latency and reduced responsiveness. In many cases, predictions are generated in batch mode rather than in real time, limiting their usefulness for proactive error prevention. Developers may receive predictions only after significant delays, reducing the opportunity to address problems in early development phases.

[0012] Another problem concerns the adaptability of existing systems. Software development methodologies, programming languages, and architectural frameworks are constantly evolving, and error prediction models must adapt accordingly. Many current solutions require manual retraining and reconfiguration to remain effective, which can be time-consuming and technically demanding. Without continuous adaptation, model accuracy can decrease over time, leading to unreliable predictions.

[0013] Furthermore, existing systems often struggle with unbalanced datasets where faulty modules represent only a small portion of the total source code. This imbalance can cause predictive models to tend towards error-free classifications, thus reducing their ability to detect actual errors. Addressing such imbalances requires advanced training strategies and computational resources that are not always readily available in conventional systems.

[0014] Security and data privacy concerns also arise when fault prediction systems rely on centralized data storage or external cloud-based processing. Sensitive source code and development data can be exposed to unauthorized access if they are not adequately protected. Organizations with strict confidentiality requirements may therefore be hesitant to use cloud-based prediction services, which limits the adoption of such technologies.

[0015] Furthermore, the interpretability of existing AI models presents developers with challenges. Many machine learning models function like black-box systems, delivering predictions without clearly explaining how these conclusions were reached. This lack of transparency can erode developer confidence and make it difficult to take corrective action based on the predictions. Developers may be hesitant to modify code based on predictions whose rationale is unclear.

[0016] Another drawback of current solutions is the lack of dedicated hardware support for error prediction. Most implementations rely on general-purpose computers that are not optimized for the continuous processing of large volumes of software development data. This results in higher energy consumption, longer processing times, and reduced performance in real-time development environments. The absence of an integrated, hardware-based architecture specifically for error prediction reduces efficiency and limits the possibilities for continuous monitoring and learning.

[0017] The lack of a unified, dedicated computer system that integrates all phases of fault prediction into a cohesive hardware architecture represents a significant technological gap. While existing tools offer partial solutions, they do not constitute a comprehensive system capable of capturing development data, processing complex software functions, training predictive models, and generating real-time fault predictions within a single, integrated machine environment. This gap underscores the need for an advanced computer system specifically designed for AI-based software fault prediction with improved efficiency, scalability, and adaptability. Summary

[0018] This disclosure describes a computing device implemented as an integrated machine structure for AI-assisted prediction of software defects. The device comprises a hardware-based processing architecture with integrated memory, data acquisition modules, feature extraction circuits, a model training engine, and an inference subsystem. It is configured to receive software-related data such as source code metrics, execution logs, version control information, and test reports, and to transform this data into structured feature representations. The processed features are then used by an embedded AI engine to predict the probability, location, and severity of software defects. The device also includes feedback-driven learning mechanisms that continuously improve the prediction accuracy using newly generated development data.

[0019] The present invention relates to a computer device for AI-assisted prediction of software errors. This device is designed as an integrated machine capable of identifying potential software errors through intelligent analysis of software-related data in early development phases. The invention aims to provide a dedicated hardware-based system that can receive, process, and analyze various development artifacts, such as source code structures, software metrics, historical error logs, and execution patterns, to determine the probability of errors with improved accuracy and reliability.

[0020] A further objective of the invention is to provide a computer that integrates data acquisition, preprocessing, feature extraction, model training, and predictive inference into a single structural framework to minimize delays, reduce processing overhead, and ensure continuous operation. The invention aims to eliminate dependence on fragmented software tools by integrating all functional components into a unified machine architecture capable of processing large volumes of software development data in real time.

[0021] Another objective of the invention is to provide a device that, using embedded artificial intelligence, can learn from historical and ongoing development activities, thereby enabling adaptive prediction of error-prone modules. The invention aims to ensure that the predictive models improve over time by continuously integrating new data, error reports, and code changes into the learning process without requiring extensive manual intervention.

[0022] A further objective of the invention is to provide a computational structure that improves prediction accuracy by generating multidimensional feature representations. These feature representations are based on code complexity, change frequency, structural dependencies, and runtime behavior. The invention aims to detect logical inconsistencies and potential errors early on before software deployment, thereby reducing maintenance costs and increasing software reliability.

[0023] Another objective of the invention is to provide a robust and scalable machine that supports the high-speed processing of large and constantly changing code repositories through hardware-accelerated computations. The device is intended to operate efficiently in environments with continuous integration and frequent software updates, ensuring that predictions remain current and relevant throughout the development process.

[0024] Another objective of the invention is to provide a computer that supports the automated integration of feedback. Confirmed error information is fed back into the learning framework to improve prediction accuracy. This adaptive capability is intended to increase the consistency and reliability of error prediction results across different software projects and development environments.

[0025] A further objective of the invention is to provide a structurally integrated system that improves transparency in error prediction by linking prediction results with identifiable software modules and measurable feature attributes. This enables development teams to better understand the factors contributing to predicted errors and to take informed corrective actions.

[0026] Another objective of the invention is to provide a secure and self-contained computer system that can be operated within controlled development environments without external processing services. The invention aims to support the local processing of sensitive source code data, thereby reducing the risk of disclosure and maintaining confidentiality.

[0027] A further objective of the invention is to provide a flexible machine structure that can adapt to different programming environments, development methods, and software architectures through configurable processing and storage components. This adaptability ensures the effectiveness of the device in various application areas and evolving technological frameworks.

[0028] Another objective of the invention is to provide a device that improves the overall quality of software by enabling proactive detection and prevention of errors instead of reactive correction after deployment. By integrating intelligent predictive mechanisms into a dedicated hardware structure, the invention aims to increase development efficiency, reduce system failures, and support the development of more reliable software systems. BRIEF DESCRIPTION OF THE IMAGE

[0029] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a computing device for predicting software errors using artificial intelligence.

[0030] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0031] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0032] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0033] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0034] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0036] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0037] Fig.Figure 1 shows a block diagram of a computer system for AI-based prediction of software errors.System 100 comprises: at least one processing unit (102) connected to non-volatile memory; a data acquisition unit (104) that receives software development artifacts such as source code files, change histories, execution logs, compilation results, and error annotations from one or more external computer systems via a communication interface; a preprocessing processor (106) that normalizes the received software development artifacts through token separation, structural analysis, and metric alignment, and structurally transforms them into standardized data representations; and a feature extraction processor (108) connected to the preprocessing processor that generates multidimensional feature representations corresponding to attributes of software complexity, dependency relationships, code change frequency, and execution behavior patterns.A learning processor (110), which is stored in non-volatile memory and can be executed by the processing unit, is configured to create predictive models using historical feature representations and corresponding error indicators through iterative parameter adjustment. A prediction processor (112) is configured to apply the prediction models to newly generated feature representations to determine a probability value for the occurrence of software errors in identified software components. A feedback processing unit (114) is configured to receive confirmed error information and update the prediction models by integrating newly received error results into stored training data.An output interface unit (116) is configured to output prediction results, specifying the probability of failure, the affected software components, and the severity estimate, to an external computing environment.

[0038] In one embodiment, the data acquisition unit (104) comprises a communication interface circuit configured to establish bidirectional data transmission with version control systems, test environments, and execution monitoring systems. The communication interface circuit includes a buffer circuit that temporarily stores incoming data streams prior to preprocessing to ensure synchronized data acquisition.

[0039] In one embodiment, the preprocessing processor (106) is configured to perform a structured analysis of the source code by identifying syntactic elements, control flow structures and functional boundaries, and to convert unstructured execution logs and textual error records into machine-readable structured records through text normalization and context segmentation.

[0040] In one embodiment, the feature extraction processor (108) is configured to calculate complexity indicators such as nesting depth, dependency count, change density and execution path frequency, and to generate feature vectors that represent the temporal evolution of software components based on a chronological analysis of the code change history.

[0041] In one embodiment, the learning processor (110) is configured to perform iterative model construction by adjusting stored parameter values ​​based on a comparison between predicted error results and actual error annotations, wherein the learning processor manages multiple model states corresponding to different software project contexts within the non-volatile storage unit.

[0042] In one embodiment, the prediction processor (112) is configured to analyze feature vectors associated with newly received software artifacts and to generate probability estimates for component-level errors by comparing the feature vectors with stored prediction model parameters through computational similarity evaluation operations.

[0043] In one embodiment, the feedback processing unit (114) is configured to receive error confirmation data generated after software deployment and to integrate this confirmation data into stored training data sets by updating appropriate feature mappings, thereby enabling a continuous improvement in prediction accuracy.

[0044] In an embodiment further comprising a control processor configured to coordinate the sequential and parallel execution of operations performed by the preprocessing processor, feature extraction processor, lem processor and prediction processor, and to manage the data flow between the non-volatile storage unit and the processing unit by controlled memory allocation and access planning.

[0045] In one embodiment, the feature extraction processor (108) is further configured to identify structural relationships between software components by analyzing function call patterns, data access dependencies and hierarchical organization within the source code and to encode these relationships into structured feature representations that are stored in the memory unit.

[0046] In one embodiment, the learning processor (110) is configured to manage historical training data sets corresponding to multiple versions of software systems and to selectively prioritize newer data sets by assigning a higher adjustment weight to newly received error data during model refinement operations.

[0047] The AI-powered software error prediction system operates through the coordinated interaction of a processing unit, persistent storage, and specialized processing elements. These transform software development artifacts into predictive information. The system is designed as a standalone hardware unit that captures software-related data, transforms it into structured representations, learns predictive relationships from historical error events, and generates probability estimates for newly developed or modified software components. The system's detailed functionality comprises a sequence of technical steps that together enable reliable and adaptive prediction of software errors.

[0048] During operation, the data acquisition unit receives software development artifacts from external computing environments via a communication interface. These artifacts include source code files, change histories, execution logs, compilation results, and historical error messages. The acquired data can be received continuously or in batches and temporarily stored in a designated area of ​​non-volatile memory. The data acquisition unit performs an initial alignment by organizing the incoming data streams according to chronological sequence, software version identifiers, and component assignment, thus ensuring consistency for subsequent processing.

[0049] The preprocessing processor transforms heterogeneous data into standardized representations suitable for computer-aided analysis. The preprocessing technique performs structured parsing of the source code by identifying functions, classes, control flow blocks, and dependency structures. The processor separates tokens from the source code, removes redundant formatting information, and organizes syntactic elements into structured data segments. For execution logs and textual error records, the preprocessing processor normalizes variable text expressions by converting them into consistent semantic tokens and matching them with the associated software components.Data consistency checking is performed by detecting incomplete entries, correcting format deviations, and synchronizing time-stamped development records to ensure an accurate correlation between software changes, execution behavior, and error occurrence.

[0050] After preprocessing, the feature extraction processor generates multidimensional feature representations that characterize the structural and behavioral attributes of software components. The feature extraction technique calculates complexity indicators by analyzing nesting depth, the number of function calls, dependencies between modules, and change density across different code versions. The processor also evaluates temporal change patterns by measuring the frequency and magnitude of code changes over time, thereby identifying components that undergo frequent restructuring. Dependency mapping is achieved by identifying connections between functions, classes, and data access paths and encoding them into structured representations.Execution behavior patterns are extracted by analyzing runtime logs, including the frequency of execution paths, the locations of errors, and performance fluctuations. All extracted features are combined into feature vectors that represent the operational and structural state of individual software components.

[0051] The learning processor uses historical feature vectors and associated error annotations from non-volatile memory to build predictive models. The learning process begins by initializing parameter values ​​that represent the relationships between feature attributes and error occurrence. The processor performs iterative adjustments by repeatedly comparing predicted results with known error labels. In each iteration, the processor adjusts the stored parameter values ​​to minimize discrepancies between predicted and actual error indicators. The adjustment process involves computationally evaluating the relationships between complexity indicators, change frequency, dependency density, and historical error patterns. Over multiple iterations, the learning processor refines the parameter mappings to capture consistent predictive patterns that point to error-prone software components.

[0052] To improve generalizability, the learning processor manages multiple parameter states corresponding to different software project contexts. The process gives greater weight to recently acquired error data by increasing the adjustment weight for new feature vectors associated with confirmed errors. This continuous learning approach allows the predictive model to adapt to changing programming practices, architectural changes, and development environments. The learning processor also maintains historical datasets across multiple software versions, enabling the system to identify long-term patterns related to error occurrence at different stages of software development.

[0053] Once predictive models are created and stored, the prediction processor applies them to newly generated feature vectors derived from incoming software artifacts. The prediction process compares each new feature vector to stored parameter relationships to calculate a probability score indicating the likelihood that a corresponding software component might contain a fault. The processor performs similarity assessments by measuring the match between new feature attributes and historical, fault-associated patterns. Based on this assessment, the processor generates a fault probability estimate and a severity score by correlating feature properties with previously observed fault effects.The severity assessment takes into account the structural significance, the scope of dependencies, and the execution relevance of the software component to categorize predicted errors according to their potential system impact.

[0054] The predictive processor operates continuously, enabling real-time evaluation of software components during development. Newly modified code sections are analyzed immediately after preprocessing and feature extraction, allowing for the early identification of error-prone areas. The output interface transmits the predictive results to external computing environments by transferring structured data that specifies error probabilities, affected components, and severity levels. The output can be displayed in formats suitable for integration into development monitoring systems or visualization interfaces.

[0055] A feedback processing unit continuously optimizes the device's predictive capability by integrating error information confirmed after testing or implementation. When actual errors are identified and verified, the feedback processing unit maps these confirmed errors to previously generated feature vectors corresponding to the same software components. This process updates stored training datasets by adding the new error information and initiating additional learning iterations. This strengthens the link between specific feature patterns and actual error outcomes, thereby improving the predictive accuracy of subsequent evaluations.

[0056] The control processor coordinates the overall operation of the device by managing execution timing, memory allocation, and data flow between the preprocessing processor, feature extraction processor, learning processor, prediction processor, and feedback unit. It ensures that preprocessing and feature extraction are completed before learning or prediction operations are initiated. Furthermore, it supports parallel execution by simultaneously analyzing multiple software components, thereby improving processing efficiency for large software repositories.

[0057] The memory management processor allocates separate memory areas within the non-volatile memory unit to manage raw software data, normalized datasets, feature representations, predictive parameter values, and feedback data. This organized memory structure enables efficient retrieval and update operations during learning and prediction cycles. When new software versions are introduced, the memory management processor stores historical data, allowing for the analysis of software development and error patterns over extended periods.

[0058] The technique implemented in the computer system supports incremental learning by continuously updating predictive relationships without requiring complete retraining on the original datasets. This is achieved by integrating newly acquired feature vectors and error confirmations into existing parameter structures. Over time, the system becomes increasingly effective at identifying subtle error indicators associated with structural complexity, frequent changes, and interdependent software components.

[0059] Through the interplay of data acquisition, preprocessing, feature generation, parameter adjustment, predictive evaluation, and feedback integration, the system establishes a self-improving, predictive mechanism for detecting software errors. The hardware-integrated architecture ensures reliable and consistent processing performance, while the adaptive learning technique allows the system to adjust to new software development trends and error characteristics. This enables the early identification of error-prone components and supports the proactive improvement of software quality in diverse development environments.

[0060] In one embodiment, the computing device is implemented as a structured machine, consisting of a housing that encloses a processing unit, non-volatile memory units, input interface modules, and output interface circuitry on a system board. The processing unit is a hardware computing core configured to execute machine instructions related to artificial intelligence operations, data analysis, and predictive analytics. The memory unit is physically connected to the processing unit and stores trained model parameters, software datasets, intermediate representations of features, and system instructions required for operation.

[0061] The device features a data acquisition interface configured as a hardware-based communication module, capable of receiving structured and unstructured software development data from external computing environments. This data includes source code files, change histories, software metrics, compilation logs, error logs, runtime data, and test results. The interface supports high data throughput and performs initial buffering and formatting using integrated data processing circuitry.

[0062] A preprocessing engine is integrated into the device and implemented as a hardware-based processing block that normalizes incoming data streams. This engine transforms heterogeneous software artifacts into structured, analysis-ready representations. It performs source code tokenization, syntactic pattern extraction, metric scale normalization, and temporal alignment of development data. This ensures that the input data is transformed into a uniform format before feature extraction.

[0063] A feature extraction module is implemented as a dedicated computational subsystem that derives quantitative descriptors from normalized software data. The module calculates complexity measures, dependency relationships, change frequency indicators, and structural attributes of software modules. It contains logic circuits that identify relationships between functions, classes, and modules and generate multidimensional feature vectors that characterize the behavior and structure of software components.

[0064] The computing device also includes an AI engine integrated into the hardware architecture. This engine consists of a model training unit and an inference unit. The model training unit receives feature vectors and historical error data and trains predictive models using machine learning techniques. The training process is performed by hardware-accelerated numerical processing units capable of matrix operations, gradient-based optimization, and parameter fitting. The trained model parameters are stored in memory for later inference operations.

[0065] The inference unit is configured to apply the trained model to newly received feature data to estimate the probability of errors in software components. The inference unit generates predictive outputs that indicate potentially error-prone modules, the probability of the severity, and confidence levels. These outputs are transmitted to an output interface, which may include display circuits, communication transmitters, or integration interfaces for external development tools.

[0066] In one embodiment, the device includes a feedback integration system that captures confirmed errors after deployment and integrates them into the learning cycle. This subsystem receives updated error reports and verification results and feeds them back to the training engine. The hardware-based iterative update mechanism allows the model to adapt over time to changing coding patterns, development practices, and project-specific characteristics.

[0067] The device architecture also includes a control unit that handles the synchronization between the data acquisition interface, preprocessing module, feature extraction module, and AI processing module. The control unit ensures the sequential and parallel coordination of operations, manages memory allocation, and controls the execution time to optimize throughput and prediction accuracy.

[0068] In implementation, the computing device is built either as a standalone unit or as an integrated module within a development infrastructure. The physical structure includes voltage regulation circuits, thermal management components, and internal high-speed buses that connect the processing unit, memory, and functional modules. The modular design of the hardware components enables scalable deployment in environments ranging from individual development workstations to enterprise software development infrastructures.

[0069] The system architecture allows for expansion through additional learning models, improved feature extraction logic, and increased storage capacity, thus ensuring the continuous development of predictive capabilities. The hardware-based design guarantees consistent performance, low latency, and reliable operation even under the high data loads typical of large-scale software development environments.

[0070] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0071] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A computer device for AI-based prediction of software errors. 102 processing units 104 Data acquisition unit 106 Preprocessing processor 108 Feature Extraction Processor 110 Learning Processor 112 Prediction Processor 114 Feedback processing unit 116 Output interface unit

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