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Developing a Testability Prediction Model for High Complexity Software using Regression Analysis
http://doi.org/10.5626/JOK.2023.50.2.162
Testability is the degree to which the software supports testing in a given test context. Early prediction of testability can help developers identify software components that require a lot of effort to ensure software quality, plan testing activities, and recognize the need for refactoring to reduce testing effort. Existing studies have been conducted to predict testability by performing regression analysis using software metrics and code coverage. These studies used training data with a large proportion of simple software structures. However, prediction models trained with imbalanced data with a large proportion of simple structures may have low testability prediction accuracy of high complexity software. We used the training data generated based on the metric acceptance criteria of industry domain standards to build a prediction model considering high complexity software. As a result of building a testability prediction model using three regression analyses, we construct a predictive model with a branch coverage error of about 4.4% and a coefficient of determination of 0.86.
Transformation Method for a State Machine to Increase Code Coverage
YoungDong Yoon, HyunJae Choi, HeungSeok Chae
Model-based testing is a technique for performing the test by using a model that represents the behavior of the system as a system specification. Industrial domains such as automotive, military/aerospace, medical, railway and nuclear power generation require model-based testing and code coverage-based testing to improve the quality of software. Despite the fact that both model-based testing and code coverage-based testing are required, difficulty in achieving a high coverage using model-based testing caused by the abstraction level difference between the test model and the source code, results in the need for performing model-based testing separately. In this study, to overcome the limitations of the existing model-based testing, we proposed the state machine transformation method to effectively improve the code coverage using the protocol state machine, one of the typical modeling methods is used as the test model in model-based testing, as the test model. In addition, we performed a case study of both systems and analyzed the effectiveness of the proposed method.
A Comparison of the Search Based Testing Algorithm with Metrics
Search-Based Software Testing (SBST) is an effective technique for test data generation on large domain size. Although the performance of SBST seems to be affected by the structural characteristics of Software Under Test (SUT), studies for the comparison of SBST techniques considering structural characteristics are rare. In addition to the comparison study for SBST, we analyzed the best algorithm with different structural characteristics of SUT. For the generalization of experimental results, we automatically generated 19,800 SUTs by combining four metrics, which are expected to affect the performance of SBST. According to the experiment results, Genetic algorithm showed the best performance for SUTs with high complexity and test data evaluation with count ≤ 20,000. On the other hand, the genetic simulated annealing and the simulated annealing showed relatively better performance for SUTs with high complexity and test data evaluation with count ≥ 50,000. Genetic simulated annealing, simulated annealing and hill climbing showed better performance for SUTs with low complexity.
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