Using semi-supervised clustering to improve regression test selection techniques

Chen, Songyu; Chen, Zhenyu; Zhao, Zhihong; Xu, Bin; Feng, Yang

doi:10.1109/icst.2011.38

Cited by 55 publications

(34 citation statements)

References 31 publications

(51 reference statements)

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“…F‐measure is used to evaluate the proposed system. It evaluates the integrative benefit of the precision and recall measures by the combination of the two parameters .

F_italicMeasure = ((), 2 * italicPrecision * italicRecall) true/ ((), italicPrecision prefix+ italicRecall)

…”

Section: Results and Evaluation Parametersmentioning

confidence: 99%

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Cluster‐based test cases prioritization and selection technique for agile regression testing

Kandil

Moussa

Badr

2016

J Software Evolu Process

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Regression testing repeatedly executes test cases of previous builds to validate that the original features are not affected with any new changes. In recent years, regression testing has seen a remarkable progress with the increasing popularity of agile methods, which stress the central role of regression testing in maintaining software quality. The optimum case for regression testing in agile context is to run regression set at the end of each sprint and release, which requires a lot of cost and time. In this paper, we present an automated agile regression testing approach on both the sprints and release levels. The proposed approach addresses both weighted sprint test cases prioritization technique, which prioritizes test cases based on several parameters having real practical weight for testers, and Cluster‐based Release Test cases Selection technique that clusters user stories based on the similarity of covered modules to solve the scalability issue. Test cases are then selected based on issues logged for failed test cases using text mining techniques. The proposed approach achieves enhancement for both the prioritization and selection of test cases for agile regression testing. Copyright © 2016 John Wiley & Sons, Ltd.

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“…F‐measure is used to evaluate the proposed system. It evaluates the integrative benefit of the precision and recall measures by the combination of the two parameters .

F_italicMeasure = ((), 2 * italicPrecision * italicRecall) true/ ((), italicPrecision prefix+ italicRecall)

…”

Section: Results and Evaluation Parametersmentioning

confidence: 99%

“…In addition, Chen et al . introduced a semi‐supervised learning technique for regression test selection. This approach introduced a semi‐supervised clustering method named semi‐supervised K‐means, which combined semi‐supervised nonlinear dimensionality reduction and K‐means.…”

Section: Related Workmentioning

confidence: 99%

Cluster‐based test cases prioritization and selection technique for agile regression testing

Kandil

Moussa

Badr

2016

J Software Evolu Process

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“…In [10], unsupervised learning is used for failure report classification and supervised learning is used for feature selection to improve unsupervised learning. Recently, semi-supervised learning is introduced to assist test case selection [3]. As much as we know, all of the existing efforts of software behavior learning are based on single-label learning.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we will focus on supervised learning. A recent effort on semi-supervised learning of software behavior is proposed to deal with both label and unlabel data simultaneously [3]. Multi-label learning could also be generalized to unsupervised learning, and even semi-supervised learning.…”

Section: B Contributionmentioning

confidence: 99%

Multi-label software behavior learning

Feng¹,

Chen²

2012

2012 34th International Conference on Software Engineering (ICSE)

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Abstract-Software behavior learning is an important task in software engineering. Software behavior is usually represented as a program execution. It is expected that similar executions have similar behavior, i.e. revealing the same faults. Single-label learning has been used to assign a single label (fault) to a failing execution in the existing efforts. However, a failing execution may be caused by several faults simultaneously. Hence, it needs to assign multiple labels to support software engineering tasks in practice. In this paper, we present multi-label software behavior learning. A well-known multi-label learning algorithm ML-KNN is introduced to achieve comprehensive learning of software behavior. We conducted a preliminary experiment on two industrial programs: flex and grep. The experimental results show that multi-label learning can produce more precise and complete results than single-label learning.

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“…This study was conducted using industrial software product as Microsoft Dynamics X and may not apply on other products. Songyu Chen et al [6] introduced a semisupervised clustering method named semi-supervised Kmeans (SSKM), which combined SSDR and K-means. However, it was applied on a small set of subject programs.…”

Section: Data Mining In Regression Testingmentioning

confidence: 99%

Regression Testing Approach for Large-Scale Systems

Kandil

Moussa

Badr

2014

2014 IEEE International Symposium on Software Reliability Engineering Workshops

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Regression testing is an important and expensive activity that is undertaken every time a program is modified to ensure that the changes do not introduce new bugs into previously validated code. Instead of re-running all test cases, diơerent approaches were studied to solve regression testing problems. Data mining techniques are introduced to solve regression testing problems with large-scale systems containing huge sets of test cases, as different data mining techniques were studied to group test cases with similar features. Dealing with groups of test cases instead of each test case separately helped to solve regression testing scalability issues. In this paper, we propose a new methodology for regression testing of large-scale systems using data mining techniques to prioritize and select test cases based on their coverage criteria and fault history.

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Using semi-supervised clustering to improve regression test selection techniques

Cited by 55 publications

References 31 publications

Cluster‐based test cases prioritization and selection technique for agile regression testing

Cluster‐based test cases prioritization and selection technique for agile regression testing

Multi-label software behavior learning

Regression Testing Approach for Large-Scale Systems

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