An ensemble meta-estimator to predict source code testability

Zakeri‐Nasrabadi, Morteza; Parsa, Saeed

doi:10.1016/j.asoc.2022.109562

Cited by 10 publications

(11 citation statements)

References 55 publications

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“…Albeit, our evaluation by experts confirms that it also helps manual testing due to using coarse-grain refactoring operations focused on smelly parts of the program. Zakeri and Parsa have proposed prediction models, respectively, for source code Coverageability [40], [41] and testability measurement [85]. However, they do not propose any testability improvement approach based on the prediction models.…”

Section: Testability Measurement and Improvement Approachesmentioning

confidence: 99%

Testability-driven development: An improvement to the TDD efficiency

Parsa,

Zakeri-Nasrabadi,

Turhan

2025

Computer Standards & Interfaces

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Section: Testability Measurement and Improvement Approachesmentioning

confidence: 99%

Testability-driven development: An improvement to the TDD efficiency

Parsa,

Zakeri-Nasrabadi,

Turhan

2025

Computer Standards & Interfaces

Self Cite

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“…Existing studies on metric-based testability prediction have focused mainly on testability in terms of test effort, as defined by ISO 9126, and test effectiveness, as defined by ISO 25010. Studies have been conducted on testing efforts using test information [3]- [6], [10], [11] and on testing effectiveness using code coverage [7]- [9].…”

Section: Related Workmentioning

confidence: 99%

“…Zakeri and Nasrabadi [8], [9] constructed a testability prediction model based on object-oriented metrics for 110 opensource Java systems. In [8], five regression algorithms were used to build a prediction model using the product of the average branch coverage, statement coverage, and minimum test case ratio to improve the coverage as a measure of testability.…”

Section: Related Workmentioning

confidence: 99%

“…Among the recent testability prediction methods, existing studies use code coverage as a measure of testability in terms of test effectiveness [7]- [9]. Code coverage is one of the representative indicators that can measure test effectiveness as a test criterion required by ISO 26262 [12], IEC 60601 [13], EN 50129 [14], IEC 61508 [15], and DO 178C [16] industry standards.…”

Section: Introductionmentioning

confidence: 99%

“…The software used in previous studies often does not have a complex code structure; therefore, these studies were conducted using a high proportion of low-complexity source code. For example, in SF110 [17], which consists of 110 open-source Java projects used in existing studies [8], [9], only 2.9% of the non-abstract methods have a cyclomatic complexity (CC) exceeding eight or a number of structuring levels (SL) exceeding four.…”

Section: Introductionmentioning

confidence: 99%

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Development of Testability Prediction Models Considering Complexity Diversity for C Programs

Choi,

Chae

2023

IEEE Access

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Testability prediction can help developers identify software components that require significant effort to ensure software quality, plan test activities, and recognize the need for refactoring to reduce the test effort. Previous studies have predicted code coverage as a measure of testability based on software metrics. However, these studies have primarily used object-oriented software with simple code structures. Industrial software developed using C is often more complex than the object-oriented software used in these studies. Models trained primarily on low-complexity training data may have insufficient training for the testability of high-complexity software. In this study, we developed a testability prediction model for C programs by considering the complexity diversity. We analyzed the impact of the complexity of the training/test data on the testability prediction model for C programs. The results showed that the model with the best performance achieves an MAE of 7.436 and an R 2 of 0.813. Moreover, the results demonstrated that as the complexity diversity of the training data decreased, MAE increased from 5.203 to 6.361, and R 2 decreased from 0.809 to 0.725. Furthermore, the performance of the model trained with low complexity-diversity deteriorated as the complexity level of the test data increased, with MAE increasing from 3.498 to 6.631, and R 2 decreasing from 0.841 to 0.687. Additionally, in the correlation analysis between the model performance and the difference in the complexity of the training and test data, a strong correlation was observed, with MAE of 0.898 and R 2 of -0.848.

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Testability Driven Development (TsDD)

Parsa

2023

Software Testing Automation

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An ensemble meta-estimator to predict source code testability

Cited by 10 publications

References 55 publications

Testability-driven development: An improvement to the TDD efficiency

Testability-driven development: An improvement to the TDD efficiency

Development of Testability Prediction Models Considering Complexity Diversity for C Programs

Testability Driven Development (TsDD)

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