Enhanced regression testing technique for agile software development and continuous integration strategies

Ali, Sadia Samar; Hafeez, Yaser; Hussain, Shariq; Yang, Shunkun

doi:10.1007/s11219-019-09463-4

Cited by 34 publications

(40 citation statements)

References 56 publications

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“…According to the common RTS evaluation metrics [11][12][13][14], considering the problems in the experiments, 3 metrics are proposed as follows:…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…(1) Testing time costs saving rate (ET) is used to measure the extent to which RTS techniques reduce regression testing time costs. Let TO represent total execution time costs of the original test cases, TR represent the execution time costs of the selected test cases based on RTS techniques, and TS represent the time costs of the selection process, then ET is given by equation in ( 11 (11) (2) Percentage reduction of the number of test cases (EN) is used to measure the ability of RTS techniques save testing costs only in terms of reducing the number of test cases [12,13]. Let NO represent the number of original test cases, NR represent the number of selected test cases, then ET is given by equation in (12):…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…(3) Recall (R) indicates the percentage of selected test cases relative to all failed test cases [11][12][13][14], which is used to measure the safety of RTS techniques. Let NOF represent the number of test cases revealing faults in the original test suite, NSF represent the number of test cases revealing faults in the selected test suite based on RTS techniques, then R is given by equation in ( 13):…”

Section: Evaluation Metricsmentioning

confidence: 99%

“… NSF R 100% NOF (13) (4) Precision (P) indicates the accuracy with which test cases were selected to be rerun [11][12][13][14]. Since NR represents the number of selected test cases, then P is given by equation in (14):…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Therefore, to select the expected test cases, change impact analysis is a necessary step in RTS researches. From the state of art of regressing testing researches [8][9][10][11], the artifacts such as specifications, design models and code files are required in most RTS techniques to analyze witch test cases are affected by the changes. The empirical study shows that [10], with the JAVA codes presented, the RTS technique can help to identify and select only the 30% -50% of the original testing cases based on change impact analysis of the code files.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Microservice Regression Testing Selection Approach Based on Belief Propagation

Chen¹,

Wu²,

Yang³

et al. 2021

Preprint

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Regression testing is required in each iteration of microservice systems. Regression testing selection, which reduces testing costs by selecting a subset from the original test cases, is one of the main techniques to optimize regression testing. Existing techniques mainly rely on the information retrieved from artifacts such as code files and system models. For microservice systems with service autonomy, development method diversity and a large amount of services, such artifacts are too difficultly obtained and costly processed to apply those approaches. This paper presents a regression testing selection approach called MRTS-BP, which needs the API gateway layer logs instead of code files and system models as inputs. By parsing the API gateway layer logs, our approach establishes the service dependency matrix, which in further is transformed into a directed graph with the services as nodes. Then, to find out which test cases are affected by service changes, an algorithm based on belief propagation is presented to compute the quantitative results of service-change propagation from the directed graph. Finally, the relationships between original test cases and service-change propagation results are established to select test cases with three strategies. To evaluate the efficiency of MRTS-BP, the empirical study based on four microservice systems is presented. A typical technique RTS-CFG is compared with MRTS-CFG and four experiments are setup to investigate four research questions. The results show that MRTS-BP can not only reduce the number of test cases by half compared with the retest-all strategy while ensuring the safety, but also save at least 20% testing time costs more than that of RTS-CFG. MRTS-BP is more practical than the techniques relying on the artifacts when the latter cannot be implemented due to the artifacts are difficult to obtain and process.

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“…According to the common RTS evaluation metrics [11][12][13][14], considering the problems in the experiments, 3 metrics are proposed as follows:…”

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Microservice Regression Testing Selection Approach Based on Belief Propagation

Chen¹,

Wu²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Factors and Techniques for Software Quality Assurance in Agile Software Development

Kaur¹,

Kaur²,

Harnal³

et al. 2023

Agile Software Development

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DevOpRET: Continuous reliability testing in DevOps

Bertolino

Angelis

Guerriero

et al. 2020

J Software Evolu Process

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To enter the production stage, in DevOps practices candidate software releases have to pass quality gates, where they are assessed to meet established target values for key indicators of interest. We believe software reliability should be an important such indicator, as it greatly contributes to the end‐user satisfaction. We propose DevOpRET, an approach for reliability testing as part of the acceptance testing stage in DevOps. DevOpRET relies on operational‐profile–based testing, a common reliability assessment technique. DevOpRET leverages usage and failure data monitored in operations to continuously refine its estimate. We evaluate accuracy and efficiency of DevOpRET through controlled experiments with a real‐world open source platform and with a microservice architectures benchmark. The results show that DevOpRET provides accurate and efficient estimates of the true reliability over subsequent DevOps cycles.

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Enhanced regression testing technique for agile software development and continuous integration strategies

Cited by 34 publications

References 56 publications

A Microservice Regression Testing Selection Approach Based on Belief Propagation

A Microservice Regression Testing Selection Approach Based on Belief Propagation

Factors and Techniques for Software Quality Assurance in Agile Software Development

DevOpRET: Continuous reliability testing in DevOps

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