Test project risk assessment method, device, electronic equipment, and storage medium

Abstract

The present application discloses a test project risk assessment method, device, electronic device, and storage medium, wherein the method comprises obtaining risk indicators in the test project in a preset manner, wherein the preset manner comprises manual collection and automatic crawling; based on a qualitative analysis model of attribute category division, the risk indicators are preliminarily sorted by risk level to obtain the attribute category of the risk indicators; based on the qualitative analysis model of attribute category division, the risk indicators belonging to the same attribute category are prioritized to obtain the risk assessment result. The present application realizes efficient risk assessment and risk pre-control of test projects. The present application can be used for risk assessment of software testing in the financial field.

Description

Technical Field

[0001] The present application relates to the fields of software testing technology and test project risk assessment technology, and in particular to a test project risk assessment method, device, electronic device, and storage medium. Background Art

[0002] By identifying project risks and taking quantifiable risk prevention and mitigation measures based on risk priorities, risk prevention efficiency can be improved within a limited time.

[0003] In related technologies, some methods capture quantifiable risk indicators by building an automatic risk item collection platform; other methods manually collect non-quantifiable risk indicators by setting up questionnaires, such as weighting and grading non-quantifiable risk items. However, these methods have the following shortcomings:

[0004] (1) In the technical solution of building an automatic risk item collection platform, the quantifiable indicators of project test risks in the system are mainly crawled by writing automated collection scripts. However, the test process data that is difficult to capture automatically cannot be automatically obtained, so there are limitations.

[0005] (2) In the technical solution of setting up questionnaires to manually collect non-quantifiable risk indicators, the entropy weight method, hierarchical analysis method and other questionnaire design methods are mainly used to weight and grade the indicators that are difficult to quantify. The questionnaires are highly complex, the questions are cumbersome, and there is a lack of effective integration with risk items that can be automatically identified. Summary of the invention

[0006] The embodiments of the present application provide a test project risk assessment method, device, electronic device, and storage medium to establish a risk test assessment system, thereby efficiently handling and preventing and resolving potential risks in the project.

[0007] The present application embodiment adopts the following technical solutions:

[0008] In a first aspect, an embodiment of the present application provides a test item risk assessment method, wherein the method comprises:

[0009] The risk indicators in the test project are obtained by using a preset method, wherein the preset method includes manual collection and automatic crawling;

[0010] Based on the qualitative analysis model of attribute classification, the risk indicators are preliminarily ranked according to risk levels to obtain the attribute categories of the risk indicators;

[0011] Based on the qualitative analysis model of attribute category division, the priorities of risk indicators belonging to the same attribute category are sorted to obtain risk assessment results.

[0012] In some embodiments, the risk indicators in the test project are obtained in a preset manner, and the preset manner includes manual collection and automatic crawling, including:

[0013] Run automated scripts to automatically crawl quantifiable risk indicators in test projects;

[0014] By using a preset threshold determination method, it is determined whether the value of the risk quantifiable indicator falls within the risk range;

[0015] For risk indicators whose values ​​of the risk quantifiable indicators are judged to be within the risk range, a preliminary risk level ranking is performed together with the manually collected risk indicators.

[0016] In some embodiments, the qualitative analysis model based on attribute classification performs preliminary risk level sorting on the risk indicators to obtain attribute categories of the risk indicators, including:

[0017] According to the qualitative analysis model of attribute classification, the attribute categories of risk items are classified from positive and negative aspects, the risk indicator attributes are obtained in the dimensions of user interaction and risk impact, and the risk indicator attributes are preliminarily ranked according to risk levels to obtain the attribute categories of risk indicators;

[0018] The qualitative analysis model based on the attribute category classification ranks the priorities of the risk indicators belonging to the same attribute category, including:

[0019] According to the optimization model of the qualitative analysis model divided by the attribute categories, the priorities of the risk indicators belonging to the same attribute category are graded and sorted.

[0020] In some embodiments, the qualitative analysis model based on attribute classification performs preliminary risk level sorting on the risk indicators to obtain attribute categories of the risk indicators, including:

[0021] According to the software testing strategy risk assessment requirements, the two-dimensional quality model of the qualitative analysis model based on attribute category division is converted into a two-dimensional attribute model, wherein the horizontal axis in the two-dimensional attribute model represents the state of satisfaction or dissatisfaction of the characteristic, and the right side of the horizontal axis represents the degree of satisfaction of the characteristic, and the more to the right, the higher the satisfaction; the vertical axis in the two-dimensional attribute model represents the satisfaction of the audience, and the upper half of the vertical axis represents the satisfaction, and the higher the satisfaction, the higher the satisfaction;

[0022] According to the two-dimensional attribute model, the risk indicators are preliminarily ranked in risk level to obtain attribute categories of the risk indicators, and the attribute types of the risk indicators include at least one of the following: demand risk, technical risk, and management risk.

[0023] In some embodiments, the qualitative analysis model based on attribute category division includes a KANO model, and the method further includes:

[0024] A structured questionnaire based on software testing risk assessment is obtained according to the two-dimensional attribute model of the KANO model, with software testers as the survey subjects. The testers use it to analyze the preset risk factors and evaluate whether the preset risk factors have a promoting effect on the test project results from both positive and negative directions;

[0025] The attribute categories of the risk items obtained by the division are summarized, and the category represented by the component with the largest summarized statistical value is used as the attribute category of the risk indicator corresponding to the preset risk factor.

[0026] In some embodiments, the qualitative analysis model of attribute category division further includes: a Better-Worse coefficient calculation method, wherein the qualitative analysis model based on the attribute category division sorts the priorities of risk indicators belonging to the same attribute category to obtain a risk assessment result, including:

[0027] Based on the Better-Worse coefficient calculation method, the risk indicators within the same attribute type are prioritized. The satisfaction coefficient Better in the Better-Worse coefficient calculation method represents the customer's satisfaction after the preset risk factors in the test project are resolved, and the dissatisfaction coefficient Worse in the Better-Worse coefficient calculation method represents the customer's dissatisfaction when the test project has the preset risk factors;

[0028] Calculate the satisfaction coefficients Better and dissatisfaction coefficients Worse of multiple risk indicators, and after selecting the satisfaction coefficient Better or the dissatisfaction coefficient Worse, obtain the ranking and grading of the risk indicators;

[0029] According to the ranking and grading of the risk indicators and the risk indicator weights, a risk score is obtained as the risk assessment result.

[0030] In some embodiments, the KANO model adopts a pre-control approach to adjust the priority of the test risk items according to the actual situation in the test items, divide the attribute sets belonging to the risk items, and cannot qualitatively obtain risk indicator data.

[0031] In a second aspect, an embodiment of the present application further provides a test item risk assessment device, wherein the device comprises:

[0032] An acquisition module is used to acquire risk indicators in a test project in a preset manner, wherein the preset manner includes manual collection and automatic crawling;

[0033] A preliminary risk ranking module is used to perform preliminary risk level ranking on the risk indicators based on a qualitative analysis model of attribute category division to obtain attribute categories of the risk indicators;

[0034] The risk priority ranking module is used to rank the priorities of risk indicators belonging to the same attribute category based on the qualitative analysis model of the attribute category classification to obtain risk assessment results.

[0035] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor; and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to perform the above method.

[0036] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes the above method.

[0037] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: risk indicators in the test project can be obtained in a preset manner, and then based on the qualitative analysis model of attribute category division, the risk indicators are first preliminarily sorted in risk level to obtain the attribute category of the risk indicator. Afterwards, based on the qualitative analysis model of attribute category division, the priorities of risk indicators belonging to the same attribute category are sorted to obtain the risk assessment results. Through the above method, for non-quantifiable risk indicator data, risk items are divided into multiple attribute set classifications; and the divided attributes of the same type are centralized and prioritized, and the risk score is calculated after all indicators are obtained, thereby achieving ex ante control of risk items before the start of the test project. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0039] Figure 1 This is a flow chart of a test item risk assessment method in an embodiment of the present application;

[0040] Figure 2 Schematic diagram of the implementation principle of the test project risk assessment method in the embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of the structure of the test item risk assessment device in the embodiment of the present application;

[0042] Figure 4This is a schematic diagram of the results of the Better-Worse coefficient calculation method of the test project risk assessment method in the embodiment of the present application;

[0043] Figure 5 This is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0045] During the research, the inventors found that the patents for project risk identification in the relevant technology mainly include two types of technical solutions: (1) building an automatic risk item collection platform to capture quantifiable risk indicators; (2) setting up questionnaires to manually collect non-quantifiable risk indicators, and each of them has research deficiencies.

[0046] The method of building an automatic risk item collection platform mainly involves writing automated collection scripts to crawl quantifiable indicators of project test risks in the system. However, the test process data that is difficult to capture automatically cannot be automatically obtained, so there are limitations.

[0047] Among the methods of setting up questionnaires to manually collect non-quantifiable risk indicators, the entropy weight method, hierarchical analysis method and other questionnaire design methods are mainly used to weight and grade the difficult-to-quantify indicators. The questionnaires are highly complex, the questions are cumbersome, and there is a lack of effective combination with risk items that can be automatically identified.

[0048] In response to the shortcomings in the relevant technologies, the risk identification model integrating manual and automatic collection and the risk assessment system with KANO model weighting and grading constructed in the embodiments of the present application can effectively analyze the possibility of risk occurrence from the causes of defects, summarize the full set of risk items, and construct a risk identification and assessment system from the perspective of automatic risk identification and manual risk indicator collection. It can effectively perform statistical analysis on risk item data that cannot be directly obtained in the business system, facilitate the construction of risk scores for the current system risks, and realize pre-control of risk items before the testing phase.

[0049] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0050] The present application embodiment provides a test project risk assessment method, such as Figure 1As shown, a flow chart of a test project risk assessment method in an embodiment of the present application is provided, and the method at least includes the following steps S110 to S130:

[0051] Step S110, obtaining risk indicators in the test project in a preset manner, wherein the preset manner includes manual collection and automatic crawling.

[0052] Manual collection mainly involves collecting non-quantifiable risk indicators.

[0053] During automatic crawling, you can write a risk indicator automatic collection script to automatically collect quantifiable risk indicators, use preset thresholds to determine whether the automatically collected risk indicator values ​​are within the risk range, and include indicators within the risk range in the manual sorting, weighting and grading process.

[0054] It can be understood that the non-quantifiable and quantifiable risk indicators in the test project can be obtained through the above preset method.

[0055] Step S120 , based on the qualitative analysis model of attribute category division, the risk indicators are preliminarily ranked according to risk levels to obtain attribute categories of the risk indicators.

[0056] According to the risk indicators obtained above, a qualitative analysis model based on attribute category division is used to perform preliminary risk level ranking and obtain the attribute categories of the risk indicators.

[0057] Preferably, the qualitative analysis model based on attribute category division includes the KANO model. The KANO model itself is a qualitative analysis model for attribute category division. The model can provide the results of specific problem attribute division from the user's perspective. Its characteristics are that it can analyze the two-dimensional relationship between quality characteristics and satisfaction from both positive and negative aspects, and construct a two-dimensional structured model. For example, the risk items are preliminarily ranked in terms of risk level, a two-dimensional questionnaire is designed using the qualitative model KANO model, the survey samples are qualitatively analyzed, and the risk factor attribute categories are divided. In this way, combined with the summarized test risk system, the KANO model is used to design a two-dimensional questionnaire, which facilitates a comprehensive investigation of the sample data related to the risk items from the two perspectives of project testers' awareness of risk items and whether risk management is sufficient.

[0058] Step S130: Based on the qualitative analysis model of attribute category division, the priorities of risk indicators belonging to the same attribute category are sorted to obtain a risk assessment result.

[0059] Considering that there are often qualitative indicators that are difficult to obtain directly from the system during the project testing process, such as: risks such as demand changes. Obviously, frequent demand changes will cause higher risks to the development and testing process, and for risk items that are difficult to obtain directly from such systems. Therefore, the qualitative analysis model based on the attribute category division is used to sort the priorities of risk indicators belonging to an attribute category to obtain the risk assessment results.

[0060] Preferably, the qualitative analysis model based on attribute category division includes the KANO model and the Better-Worse coefficient calculation method. It can be understood that the Better-Worse coefficient calculation method is a method for further prioritizing the same type of attributes divided by the KANO model. The satisfaction coefficient Better refers to the satisfaction of the project business personnel after a certain risk factor in the test project is resolved; the dissatisfaction coefficient Worse indicates the dissatisfaction of the project business personnel when the test project has a certain risk factor. In this way, the Better-Worse coefficient calculation method is applied to the same type of attribute set divided by the KANO model, which facilitates the priority sorting of risk items within the same attribute set.

[0061] According to the risk assessment results, the strategy system provided in the embodiment of the present application analyzes the possibility of risk occurrence from the cause of the defect, and guides the implementation of pre-control of risk items before the test phase. Compared with the existing automated risk warning platform and the method of building a machine learning risk model, it does not rely on hard risk indicators obtained by the system, and can effectively capture risk indicators that are difficult to obtain systematically. It has fast response speed and low cost, and can be applied with other multiple risk models. It is a general paradigm solution that can be generalized and applied to various projects.

[0062] In one embodiment of the present application, the risk indicators in the test project are obtained in a preset manner, and the preset method includes manual collection and automatic crawling, including: running an automated script to automatically crawl the risk quantifiable indicators in the test project; judging whether the value of the risk quantifiable indicator falls within the risk range through a preset threshold judgment method; and performing preliminary risk level sorting on the risk indicators whose values ​​are judged to fall within the risk range together with the manually collected risk indicators.

[0063] Please refer to Figure 2 By writing automated scripts, we crawl the quantifiable indicators of test risks in the project test management system to achieve automatic collection, and use the preset threshold judgment method to determine whether the values ​​of the automatically collected risk indicators are within the risk range, and include the indicators within the risk range in the manual sorting, weighting and grading process.

[0064] Taking the KANO model as an example, the quantifiable risk indicators of the system, such as use case pass rate, use case coverage, defect closure rate, serious defect rate, etc., are obtained by writing automated scripts. The specific value of the risk threshold is set by expert scoring, and a program is written to determine when a risk indicator value is higher than a given risk threshold. The risk indicator value at that moment is recorded, and it is used together with the manually collected indicators using the KANO model for weighting and risk assessment.

[0065] In the above steps, the automatically collected quantifiable test risk indicators are judged by adopting the preset threshold judgment method, and the risk indicators exceeding the threshold are included in the risk range, and are sorted, weighted and graded together with the manually reported risk indicators.

[0066] In one embodiment of the present application, the qualitative analysis model based on attribute category division performs preliminary risk level sorting on the risk indicators to obtain attribute categories of the risk indicators, including: according to the qualitative analysis model based on attribute category division, the attribute categories of the risk items are divided from positive and negative aspects, the risk indicator attributes are obtained in the dimensions of user interaction and risk impact, and the risk indicator attributes are preliminarily sorted in risk level to obtain attribute categories of the risk indicators; the qualitative analysis model based on the attribute category division ranks the priorities of the risk indicators belonging to the same attribute category, including: according to the optimization model of the qualitative analysis model based on the attribute category division, the priorities of the risk indicators belonging to the same attribute category are graded and sorted.

[0067] Please refer to Figure 2 , taking the KANO model as an example, the qualitative analysis model of attribute classification divides the attribute categories of risk items from both positive and negative aspects, summarizes the attributes of risk items from the perspective of user interaction and risk impact, and then combines the Better-Worse coefficient calculation method to rank the attributes of the risk items in the test process. For the risk indicators that are automatically pulled and judged to be within the risk range by threshold, they should be included in the KANO model risk assessment and grading process, combined with the test life cycle, integrating the risk identification model of manual and automated collection and the risk assessment system of weighted grading, and giving the current system risk score.

[0068] In one embodiment of the present application, the qualitative analysis model based on attribute category division performs preliminary risk level sorting on the risk indicators to obtain attribute categories of the risk indicators, including: according to the risk assessment requirements of the software testing strategy, the two-dimensional quality model of the qualitative analysis model based on attribute category division is converted into a two-dimensional attribute model, wherein the horizontal axis in the two-dimensional attribute model represents the status of characteristic satisfaction or non-satisfaction, and the right side of the horizontal axis is the degree of characteristic satisfaction, and the higher the right, the higher the satisfaction; the vertical axis in the two-dimensional attribute model represents the audience's satisfaction, and the upper half of the vertical axis represents the satisfaction, and the higher the satisfaction, the higher the satisfaction; according to the two-dimensional attribute model, the risk indicators are preliminarily sorted by risk level to obtain attribute categories of the risk indicators, and the attribute types of the risk indicators include at least one of the following: demand risk, technical risk, and management risk.

[0069] Please refer to Figure 2 ,For the first manual collection and the automatically crawled indicators that are judged to be above a given threshold, the KANO model is selected for weight grading.,As a useful tool for classifying and prioritizing user needs, the KANO model can reflect the nonlinear relationship between system performance and user satisfaction,based on analyzing the impact of user needs on user satisfaction.

[0070] It should be noted that the KANO two-dimensional quality model needs to be converted for the software testing strategy risk assessment problem in the embodiment of this application. First, the KANO two-dimensional quality model is generalized and converted into a KANO two-dimensional attribute model. The horizontal axis represents the state of satisfaction or dissatisfaction of the characteristic, and the right side of the horizontal axis is the degree of satisfaction of the characteristic. The more to the right, the higher the degree of satisfaction; conversely, the more to the left, the higher the degree of dissatisfaction. The vertical axis represents the satisfaction of the audience. The upper half of the vertical axis represents the satisfaction. The higher it is, the higher the satisfaction; conversely, the lower it is, the higher the degree of dissatisfaction.

[0071] According to the KANO model's dual-dimensional research requirements for risk factors, combined with the summary of common risks collected in a preset way, a set of software testing strategy project risk factors is formed on the basis of ensuring the comprehensiveness of the evaluation content, as shown in Table 1.

[0072] Table 1: Software testing project risk factor set

[0073]

[0074]

[0075] The above method, combined with the summarized test risk system, uses the KANO model to design a two-dimensional questionnaire, which is convenient for comprehensively investigating the sample data related to risk items from two perspectives: project testers' awareness of risk items and whether risk management is sufficient.

[0076] In one embodiment of the present application, the qualitative analysis model based on attribute category division includes a KANO model, and the method further includes: obtaining a structured questionnaire based on software testing risk assessment according to the two-dimensional attribute model of the KANO model, taking software testers as the survey subjects, and the testers use it to analyze the preset risk factors, and evaluate whether the preset risk factors have a promoting effect on the test project results from both positive and negative directions; summarizing the attribute categories of the risk items obtained by the division, and taking the category represented by the component with the largest summarized statistical value as the attribute category of the risk indicator corresponding to the preset risk factor.

[0077] Please refer to Figure 2 In the specific implementation, the KANO model is used to develop a structured questionnaire based on software testing risk assessment to help determine the attribute categories of various risk items in products or projects. The basic steps are:

[0078] (a) Understand product / project risk items from the perspective of project testing

[0079] (b) Design a questionnaire to understand the testers’ potential judgment on risks

[0080] (c) Implementing effective questionnaire surveys

[0081] (d) Statistical survey results, classify and summarize the attribute categories of risk items

[0082] (e) Analyze the same attribute category and calculate the priority using the Better-Worse coefficient within it

[0083] (f) Test the validity of the model.

[0084] Software testers are selected as the survey subjects. Through the analysis of specific risk factors by testers, the authors evaluate whether the risk factors have a positive or negative effect on the test project results. Taking the risk factor r1 set above as an example, the questionnaire design for this question is as follows:

[0085] Part 1: When risk factors are adequately managed, does it promote the testing program?

[0086] Table 2: Design of positive questionnaire for “r1” risk factors

[0087] Risk factors Very effective More effective Generally effective Not very effective Completely ineffective r1

[0088] Part II: When risk factors are not adequately managed, does it contribute to the testing program?

[0089] Table 3: Reverse questionnaire design for “r1” risk factors

[0090] Risk factors Very effective More effective Generally effective Not very effective Completely ineffective r1

[0091] According to the positive and negative answers, the results are summarized and statistically classified as shown in Table 1. Among them, "A" represents "attractive attribute", "M" represents "essential attribute", "O" represents one-dimensional attribute, "I" represents "irrelevant attribute", "R" represents "reverse attribute", and "Q" represents invalid evaluation. If the Q statistic is too large, it means that the sample selection is biased.

[0092] Table 4: Risk factor result determination table

[0093]

[0094] All risk factor assessment results are summarized, and the category represented by the component with the largest statistical value is the KANO risk attribute corresponding to the risk factor. Similarly, taking the risk factor "rl" as an example, it is expressed according to "attribute (statistical value)", assuming that its statistical results are "Q(0), M(10), O(9), R(1), I(24), A(30)", then the component with the largest statistical value is "A", that is, the charm attribute, indicating that "r1" belongs to the charm attribute risk factor, as shown in Table 5.

[0095] Table 5: Risk factor classification results

[0096]

[0097] The KANO risk attribute questionnaire involves two parts. The first part is a survey on the respondents' perception of the impact of risk factors in software testing projects (a questionnaire designed with positive and negative questions). The second part uses the KANO two-dimensional model to investigate whether adequate or inadequate risk factor management contributes to the smooth progress of R&D projects. The KANO two-dimensional structured method is used to ask 10 risk factors whether "whether adequate management promotes project success." To facilitate further explanation of subsequent examples, this patent provides a statistical sample of 10 risk factors and classification examples, as shown in Table 6.

[0098] Table 6: Software testing risk factor classification results

[0099] Risk Code Q M O R I A property r1 1 2 20 1 18 6 O r2 0 20 2 2 5 0 M r3 0 4 2 1 23 19 A r4 2 18 1 1 6 1 M r5 3 15 5 3 20 3 M r6 0 13 5 2 15 14 M r7 0 8 4 1 22 14 A r8 1 6 3 0 17 13 A r9 1 7 4 1 14 12 A r10 0 5 2 2 22 11 A

[0100] It can be seen from the statistical results that the statistical value Q is small, indicating that there are fewer invalid evaluations and the sample selection deviation for this survey is not large. If the KANO model risk factor classification rules are followed and the category represented by the component with the largest statistical value is selected as the attribute of the risk factor, then these 10 risk factors are all irrelevant attributes. By analyzing the questionnaire filling situation and further communicating with the respondents, it is learned that the respondents generally do not choose extreme options ("very effective" or "completely ineffective") when answering options, and the KANO model classification rules show that only when at least one of the positive and negative questions is an extreme option, the risk factor is not an irrelevant attribute. Since this survey was conducted in the form of an anonymous survey, it is not feasible to use a second round of scoring for the respondents. Adjustments are made in the embodiments of the present application to determine the risk attribute category according to the second rank of the statistic, that is, to exclude "irrelevant attributes (I)".

[0101] According to the category represented by the second largest component of the statistical value as the attribute of the risk factor, the adjusted risk attribute classification is shown in Table 7.

[0102] Table 7: KANO risk attribute ranking results

[0103] Risk ranking Risk attributes Risk Code 1 Required attributes r2，r4，r5，r6 2 Charm attribute r3，r7，r8，r9，r10 3 One-dimensional attributes r1 4 Reverse attribute ——

[0104] Combined with the above results, for the 10 typical risk factors in software testing, according to the KANO risk factor classification attributes, we can see that:

[0105] There are 4 essential attribute risk factors: r2, r4, r5, and r6. This type of risk factor is an urgent problem to be solved when conducting software testing risk management, and ranks first in the internal control priority order; there are 5 attractive attribute risk factors: r3, r7, r8, r9, and r10. The probability of success of software testing projects can be improved by effectively managing these 5 risk factors, and they rank second in the internal control priority order; there is only one "r1" for one-dimensional attribute risk factors, indicating that the risk item represented by r1 increases linearly with the overall success probability of the test project, and ranks third in the internal control priority order. The final attribute classification is mostly concentrated in the essential attributes and attractive attributes. However, after dividing the specific attribute categories for different risk items, it is also necessary to prioritize the risk items within the same attribute type.

[0106] The above steps, by applying the KANO model to the project software testing risk assessment process, adopt a pre-control approach, adjust the priority of test risk items based on the actual situation of the project, divide the risk items into 4 attribute sets, and apply them to risk indicator data that the system cannot obtain, providing a new solution for risk item division and risk identification.

[0107] In one embodiment of the present application, the qualitative analysis model for attribute category division also includes: a Better-Worse coefficient calculation method, wherein the qualitative analysis model based on the attribute category division prioritizes risk indicators belonging to the same attribute category to obtain a risk assessment result, including: prioritizing risk indicators within the same attribute type based on the Better-Worse coefficient calculation method, wherein the satisfaction coefficient Better in the Better-Worse coefficient calculation method represents the customer's satisfaction after the preset risk factors in the test project are resolved, and the dissatisfaction coefficient Worse in the Better-Worse coefficient calculation method represents the customer's dissatisfaction when the test project has preset risk factors; calculating the satisfaction coefficient Better and the dissatisfaction coefficient Worse of multiple risk indicators, and obtaining a ranking and grading of the risk indicators after selecting the satisfaction coefficient Better or the dissatisfaction coefficient Worse; obtaining a risk score as the risk assessment result according to the ranking and grading of the risk indicators and the risk indicator weights.

[0108] Please refer to Figure 2 ,like Figure 4 As shown, the preliminary sorting analysis results classify the attributes of 10 typical software testing project risk factors according to the KANO model. Since risk factors mainly focus on the links that can have a negative impact on the project, it can be expected that risk factors will be concentrated on essential attributes, attractive attributes and one-dimensional attributes. The classification results in Section 4.3 also confirm this view. According to the questionnaire survey results, the Better-Worse coefficient diagram is used in the embodiment of this application to analyze the above 10 risk factor items, and the calculation method is as follows:

[0109] Better=(A+O) / (A+O+M) (1)

[0110] Worse=-1*(O+M) / (A+O+M) (2)

[0111] Satisfaction coefficient Better refers to the degree of customer satisfaction after the risk factor in the test project is resolved. The value of Better is usually positive, which means that if a certain functional attribute is provided, user satisfaction will increase; the larger the positive value / the closer it is to 1, the greater the impact on user satisfaction, the stronger the impact on improving user satisfaction, and the faster it will rise.

[0112] The dissatisfaction coefficient Worse refers to the degree of customer dissatisfaction when the test project has this risk factor. The Worse value is usually negative, which means that if a certain functional attribute is not provided, the user's satisfaction will decrease; the more negative the value is / the closer it is to -1, the greater the impact on user dissatisfaction, the stronger the impact of reduced satisfaction, and the faster it decreases.

[0113] To facilitate the intuitiveness of the chart, the Better and Worse values ​​of the 10 risk factors are calculated respectively, and the absolute value of Better is taken. The results are as follows: Figure 4 As shown, it can be understood that the 10 risks are only examples and are not intended to limit the scope of protection of the present application.

[0114] The above steps facilitate the priority sorting of risk items within the same attribute set by applying the Better-Worse coefficient calculation method to the same attribute set divided by the KANO model.

[0115] In one embodiment of the present application, the KANO model adopts a prior control method to adjust the priority of the test risk project according to the actual situation in the test project, divide the attribute set belonging to the risk project, and cannot qualitatively obtain risk indicator data.

[0116] Further, by Figure 3 It can be seen that the method of using the Better-Worse coefficient diagram can not only obtain the four basic KANO classification attributes of software testing risk factors, but also has distinguishability under the same attributes. Considering that risk factors have a great impact on satisfaction, they also greatly affect the progress and comprehensive cost of the project. Therefore, in the same KANO attribute, this patent ranks the priorities according to the Worse coefficient (the larger the absolute value of the Worse coefficient, the higher the priority of the risk factor), and the results are shown in Table 8.

[0117] Table 8: Software testing risk factor priorities

[0118]

[0119]

[0120] According to the results in Table 8, under the premise that r2, r4, r5, and r6 all have the necessary attributes at the same time, their priorities are r2>r4>r5>r6. Therefore, when the above four risk factors exist at the same time, the "r2" risk should be given priority; similarly, among the risks that also have charm factors, the "r9" risk should be given priority.

[0121] In summary, after ranking and grading the risk indicators, the risk score value of the system can be further calculated by giving weights according to the ranking results, and the final risk score W can be obtained as shown in formula (3):

[0122]

[0123] where ω i Represents the risk index r i The weight value of .

[0124] like Figure 2 FIG. 1 is a schematic diagram of the implementation principle of the test project risk assessment method in an embodiment of the present application, which specifically includes the following steps:

[0125] Step S210, clarify the main process of risk assessment.

[0126] Step S220, focusing on the pain points of information technology system risk identification and assessment, analyzing the causes of defects, and summarizing the attributes of risk factors.

[0127] The causes of defects and risk possibilities in the test system are given by inductive summary, and the risk factor attributes and defect causes are summarized.

[0128] Step S230, automatic crawling.

[0129] Step S250: Write a script to automatically extract the quantifiable risk indicator values ​​in the system.

[0130] Step S270, using a preset threshold method to determine whether the risk indicator value is within the risk range.

[0131] Step S290: output the risk indicators that are within the risk range, and sort and weight them together with the manually collected indicators.

[0132] The preset threshold method determines whether the risk indicator values ​​automatically pulled by the system are within the risk range: write a risk indicator automatic collection script to automatically collect quantifiable risk indicators, and preset thresholds to determine whether the risk indicators are within the risk range, thereby realizing automated risk identification.

[0133] Step S240: manual collection.

[0134] Step S260, preliminary risk level ranking: applying the KANO model to assess the attributes of the test risk items.

[0135] Step S280: Analyze the priority of test risk items by combining qualitative and quantitative models.

[0136] A preliminary risk level ranking is carried out for non-quantifiable risk indicators, the KANO model is applied to assess the risk factor attributes in the test system, the risk factor attribute categories are divided, and a priority ranking system for test risk items is constructed.

[0137] Step S2100, quantitative analysis: use the Better-Worse coefficient calculation to prioritize risk factors within the same category.

[0138] Step S2120, qualitative model aspect: innovative application of KANO model to the risk factor attribute assessment of the test system.

[0139] A combination of qualitative and quantitative methods is used to further divide the priorities within the same category of risk items: the Better-Worse coefficient is used to calculate and prioritize risk factors within the same category.

[0140] Step S2140, with the support of detailed sample data, analyze the priority of risk factors and perform weighted grading.

[0141] Step S2160, construct an automatic and manual integrated risk assessment software testing strategy for the KANO model, identify risks, assess levels and give a current system risk score.

[0142] Combined with the test life cycle, it integrates the risk identification model that integrates manual and automated collection and the risk assessment system with fixed-weight and graded risk, and gives the current system risk score.

[0143] The risk assessment software testing research strategy based on the KANO model constructs a risk identification model that combines automatic collection with manual reporting. The preset threshold discrimination method is used to determine the automatically collected risk indicators within the risk range, and they are included in the sorting and weighting process of the KANO model indicators. The KANO model is applied to test risk assessment. For non-quantifiable risk indicator data, risk items are divided into multiple attribute set classifications; the Better-Worse coefficient calculation method is applied to the divided similar attribute sets for priority sorting and weighting, and finally the risk score is calculated for all indicators.

[0144] The present application embodiment also provides a test item risk assessment device 300, such as Figure 3 As shown, a schematic diagram of the structure of a test project risk assessment device in an embodiment of the present application is provided, wherein the device 300 at least includes: an acquisition module 310, a preliminary risk ranking module 320, and a risk priority ranking module 330, wherein:

[0145] In one embodiment of the present application, the acquisition module 310 is specifically used to: acquire risk indicators in the test project in a preset manner, and the preset manner includes manual collection and automatic crawling.

[0146] Manual collection mainly collects non-quantifiable risk indicators.

[0147] During automatic crawling, you can write a risk indicator automatic collection script to automatically collect quantifiable risk indicators, use preset thresholds to determine whether the automatically collected risk indicator values ​​are within the risk range, and include indicators within the risk range in the manual sorting, weighting and grading process.

[0148] It can be understood that the non-quantifiable and quantifiable risk indicators in the test project can be obtained through the above preset method.

[0149] In one embodiment of the present application, the preliminary risk ranking module 320 is specifically used to: perform preliminary risk level ranking on the risk indicators based on a qualitative analysis model of attribute category division to obtain attribute categories of the risk indicators.

[0150] According to the risk indicators obtained above, a qualitative analysis model based on attribute category division is used to perform preliminary risk level ranking and obtain the attribute categories of the risk indicators.

[0151] Preferably, the qualitative analysis model based on attribute category division includes the KANO model. The KANO model itself is a qualitative analysis model for attribute category division. The model can provide the results of specific problem attribute division from the user's perspective. Its characteristics are that it can analyze the two-dimensional relationship between quality characteristics and satisfaction from both positive and negative aspects, and construct a two-dimensional structured model. For example, the risk items are preliminarily ranked in terms of risk level, a two-dimensional questionnaire is designed using the qualitative model KANO model, the survey samples are qualitatively analyzed, and the risk factor attribute categories are divided. In this way, combined with the summarized test risk system, the KANO model is used to design a two-dimensional questionnaire, which facilitates a comprehensive investigation of the sample data related to the risk items from the two perspectives of project testers' awareness of risk items and whether risk management is sufficient.

[0152] In one embodiment of the present application, the risk priority ranking module 330 is specifically used to: sort the priorities of risk indicators belonging to the same attribute category based on the qualitative analysis model of the attribute category classification to obtain a risk assessment result.

[0153] Considering that there are often qualitative indicators that are difficult to obtain directly from the system during the project testing process, such as: risks such as demand changes. Obviously, frequent demand changes will cause higher risks to the development and testing process, and for risk items that are difficult to obtain directly from such systems. Therefore, the qualitative analysis model based on the attribute category division is used to sort the priorities of risk indicators belonging to an attribute category to obtain the risk assessment results.

[0154] Preferably, the qualitative analysis model based on attribute category division includes the KANO model and the Better-Worse coefficient calculation method. It can be understood that the Better-Worse coefficient calculation method is a method for further prioritizing the same type of attributes divided by the KANO model. The satisfaction coefficient Better refers to the satisfaction of the project business personnel after a certain risk factor in the test project is resolved; the dissatisfaction coefficient Worse indicates the dissatisfaction of the project business personnel when the test project has a certain risk factor. In this way, the Better-Worse coefficient calculation method is applied to the same type of attribute set divided by the KANO model, which facilitates the priority sorting of risk items within the same attribute set.

[0155] It can be understood that the above-mentioned test item risk assessment device can implement each step of the test item risk assessment method provided in the aforementioned embodiment, and the relevant explanations about the test item risk assessment method are applicable to the test item risk assessment device and will not be repeated here.

[0156] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0157] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0158] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0159] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a test project risk assessment device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0160] The risk indicators in the test project are obtained by using a preset method, wherein the preset method includes manual collection and automatic crawling;

[0161] Based on the qualitative analysis model of attribute classification, the risk indicators are preliminarily ranked according to risk levels to obtain the attribute categories of the risk indicators;

[0162] Based on the qualitative analysis model of attribute category division, the priorities of risk indicators belonging to the same attribute category are sorted to obtain risk assessment results.

[0163] The above application Figure 1 The method performed by the test item risk assessment device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0164] The electronic device may also perform Figure 1 The method for executing the risk assessment device of the test project is implemented in Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0165] The present application also provides a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to execute Figure 1 The method performed by the test project risk assessment device in the illustrated embodiment is specifically used to perform:

[0166] The risk indicators in the test project are obtained by using a preset method, wherein the preset method includes manual collection and automatic crawling;

[0167] Based on the qualitative analysis model of attribute classification, the risk indicators are preliminarily ranked according to risk levels to obtain the attribute categories of the risk indicators;

[0168] Based on the qualitative analysis model of attribute category division, the priorities of risk indicators belonging to the same attribute category are sorted to obtain risk assessment results.

[0169] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0173] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0174] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0175] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0176] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0177] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0178] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A test project risk assessment method, in, The method comprises: The risk indicators in the test project are obtained by using a preset method, wherein the preset method includes manual collection and automatic crawling; The risk indicators in the test project are obtained by using a preset method, which includes manual collection and automatic crawling, including: Run the automated script to automatically crawl the quantifiable risk indicators in the test project; determine whether the value of the quantifiable risk indicator falls within the risk range through a preset threshold discrimination method; perform preliminary risk ranking on the risk indicators whose values ​​are determined to fall within the risk range together with the manually collected risk indicators; Based on the qualitative analysis model of attribute classification, the risk indicators are preliminarily ranked according to risk levels to obtain the attribute categories of the risk indicators; The qualitative analysis model based on attribute classification performs preliminary risk level sorting on the risk indicators to obtain the attribute categories of the risk indicators, including: according to the software testing strategy risk assessment requirements, converting the two-dimensional quality model of the qualitative analysis model based on attribute classification into a two-dimensional attribute model; according to the two-dimensional attribute model, performing preliminary risk level sorting on the risk indicators to obtain the attribute categories of the risk indicators, wherein the attribute types of the risk indicators include at least one of the following: demand risk, technical risk, and management risk; The qualitative analysis model based on attribute category division includes a KANO model, and the method further includes: obtaining a structured questionnaire based on software testing risk assessment according to the two-dimensional attribute model of the KANO model, taking software testers as survey subjects, and the testers use it to analyze the preset risk factors and evaluate whether the preset risk factors have a promoting effect on the test project results from both positive and negative directions; summarizing the attribute categories of the risk items obtained by the division, and taking the category represented by the component with the largest summarized statistical value as the attribute category of the risk indicator corresponding to the preset risk factor; Based on the qualitative analysis model of the attribute category division, the priority of the risk indicators belonging to the same attribute category is sorted to obtain the risk assessment result; The qualitative analysis model of attribute category division also includes: Better-Worse coefficient calculation method. The qualitative analysis model based on the attribute category division ranks the priorities of risk indicators belonging to the same attribute category to obtain risk assessment results, including: based on the Better-Worse coefficient calculation method, prioritizing risk indicators within the same attribute type; calculating the satisfaction coefficient Better and the dissatisfaction coefficient Worse of multiple risk indicators, and after selecting the satisfaction coefficient Better or the dissatisfaction coefficient Worse, obtaining the ranking and grading of the risk indicators; according to the ranking and grading of the risk indicators and the risk indicator weights, obtaining the risk score as the risk assessment result.

2. The method according to claim 1, in, The qualitative analysis model based on attribute classification performs preliminary risk ranking on the risk indicators to obtain the attribute categories of the risk indicators, including: According to the qualitative analysis model of attribute classification, the attribute categories of risk items are classified from positive and negative aspects, risk indicator attributes are obtained in the dimensions of user interaction and risk impact, and the risk indicator attributes are preliminarily ranked according to risk levels to obtain attribute categories of risk indicators; The qualitative analysis model based on the attribute category classification ranks the priorities of the risk indicators belonging to the same attribute category, including: According to the optimization model of the qualitative analysis model divided by the attribute categories, the priorities of the risk indicators belonging to the same attribute category are graded and sorted.

3. The method according to claim 1, in, The KANO model adopts a pre-control approach to adjust the priority of the test risk items according to the actual situation in the test items, divide the attribute sets belonging to the risk items, and cannot qualitatively obtain risk indicator data.

4. A test project risk assessment device, in, The device comprises: An acquisition module is used to acquire risk indicators in a test project in a preset manner, wherein the preset manner includes manual collection and automatic crawling; The risk indicators in the test project are obtained by using a preset method, which includes manual collection and automatic crawling, including: Run the automated script to automatically crawl the quantifiable risk indicators in the test project; determine whether the value of the quantifiable risk indicator falls within the risk range through a preset threshold discrimination method; perform preliminary risk ranking on the risk indicators whose values ​​are determined to fall within the risk range together with the manually collected risk indicators; A preliminary risk ranking module is used to perform preliminary risk level ranking on the risk indicators based on a qualitative analysis model of attribute category division to obtain attribute categories of the risk indicators; The qualitative analysis model based on attribute classification performs preliminary risk level sorting on the risk indicators to obtain the attribute categories of the risk indicators, including: according to the software testing strategy risk assessment requirements, converting the two-dimensional quality model of the qualitative analysis model based on attribute classification into a two-dimensional attribute model; according to the two-dimensional attribute model, performing preliminary risk level sorting on the risk indicators to obtain the attribute categories of the risk indicators, wherein the attribute types of the risk indicators include at least one of the following: demand risk, technical risk, and management risk; The qualitative analysis model based on attribute category division includes a KANO model, and the method further includes: obtaining a structured questionnaire based on software testing risk assessment according to the two-dimensional attribute model of the KANO model, taking software testers as survey subjects, and the testers use it to analyze the preset risk factors and evaluate whether the preset risk factors have a promoting effect on the test project results from both positive and negative directions; summarizing the attribute categories of the risk items obtained by the division, and taking the category represented by the component with the largest summarized statistical value as the attribute category of the risk indicator corresponding to the preset risk factor; A risk priority ranking module is used to prioritize risk indicators belonging to the same attribute category based on the qualitative analysis model of the attribute category classification to obtain a risk assessment result; The qualitative analysis model of attribute category division also includes: Better-Worse coefficient calculation method. The qualitative analysis model based on the attribute category division ranks the priorities of risk indicators belonging to the same attribute category to obtain risk assessment results, including: based on the Better-Worse coefficient calculation method, prioritizing risk indicators within the same attribute type; calculating the satisfaction coefficient Better and the dissatisfaction coefficient Worse of multiple risk indicators, and after selecting the satisfaction coefficient Better or the dissatisfaction coefficient Worse, obtaining the ranking and grading of the risk indicators; according to the ranking and grading of the risk indicators and the risk indicator weights, obtaining the risk score as the risk assessment result.

5. An electronic device, include: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method of any one of claims 1 to 3.

6. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute any one of the methods of claims 1 to 3.

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