A Static Approach to Prioritizing JUnit Test Cases

Mei, Hong; Hao, Dan; Zhang, Lingming; Zhang, Lu; Zhou, Jing; Rothermel, Gregg

doi:10.1109/tse.2011.106

Cited by 129 publications

(95 citation statements)

References 36 publications

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“…H.Do et al [5] presented the importance of time constraints on test case prioritization and discovered that constraints which alters the performance of technique. Further, conducted three set of experiments which reveals the time constraints.…”

Section: Techniques Revisitedmentioning

confidence: 99%

Measuring the Effectiveness of Test Case Prioritization Techniques Based on Weight Factors

Muthusamy¹,

Seetharaman²

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: Techniques Revisitedmentioning

confidence: 99%

Measuring the Effectiveness of Test Case Prioritization Techniques Based on Weight Factors

Muthusamy¹,

Seetharaman²

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Specifically, we use our previous work [38] and its corresponding tool Jtop [67] to implement the static call graph used in this paper.…”

Section: Determining Feature Values and Failure Causes For The Sevenmentioning

confidence: 99%

Is This a Bug or an Obsolete Test?

Hao

Lan

Zhang

et al. 2013

ECOOP 2013 – Object-Oriented Programming

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Abstract. In software evolution, developers typically need to identify whether the failure of a test is due to a bug in the source code under test or the obsoleteness of the test code when they execute a test suite. Only after finding the cause of a failure can developers determine whether to fix the bug or repair the obsolete test. Researchers have proposed several techniques to automate test repair. However, test-repair techniques typically assume that test failures are always due to obsolete tests. Thus, such techniques may not be applicable in real world software evolution when developers do not know whether the failure is due to a bug or an obsolete test. To know whether the cause of a test failure lies in the source code under test or in the test code, we view this problem as a classification problem and propose an automatic approach based on machine learning. Specifically, we target Java software using the JUnit testing framework and collect a set of features that may be related to failures of tests. Using this set of features, we adopt the Best-first Decision Tree Learning algorithm to train a classifier with some existing regression test failures as training instances. Then, we use the classifier to classify future failed tests. Furthermore, we evaluated our approach using two Java programs in three scenarios (within the same version, within different versions of a program, and between different programs), and found that our approach can effectively classify the causes of failed tests.

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“…Hence, many techniques for improving regression testing have been investigated. These techniques may include, for instance, selecting only a subset of the original test suite based on modified information to achieve test target [1], [2], reducing the size of test suite by identifying and eliminating redundant test cases [3]- [5], or reordering test cases such that they can meet testing goals earlier [3], [6]- [9]. However, the former two techniques may be not safe because they have omitted test cases that could detect faults, while another one will not.…”

Section: Introductionmentioning

confidence: 99%

“…Research shows that TCP can help tester improve the rate of fault detection and the developers can start debugging work early [6], [7]. To date, various prioritization techniques have been proposed and empirically studied [3], [6]- [8]. These techniques analyze source codes and historical execution data to obtain useful information, such as code coverage of test cases, code complexity, test costs, fault severities, in the form of alone or mix and then prioritize these test cases [6], [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Coverage-Based Clustering and Scheduling Approach for Test Case Prioritization

Fan

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYRegression testing is essential for assuring the quality of a software product. Because rerunning all test cases in regression testing may be impractical under limited resources, test case prioritization is a feasible solution to optimize regression testing by reordering test cases for the current testing version. In this paper, we propose a novel test case prioritization approach that combines the clustering algorithm and the scheduling algorithm for improving the effectiveness of regression testing. By using the clustering algorithm, test cases with same or similar properties are merged into a cluster, and the scheduling algorithm helps allocate an execution priority for each test case by incorporating fault detection rates with the waiting time of test cases in candidate set. We have conducted several experiments on 12 C programs to validate the effectiveness of our proposed approach. Experimental results show that our approach is more effective than some well studied test case prioritization techniques in terms of average percentage of fault detected (APFD) values.

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A Static Approach to Prioritizing JUnit Test Cases

Cited by 129 publications

References 36 publications

Measuring the Effectiveness of Test Case Prioritization Techniques Based on Weight Factors

Measuring the Effectiveness of Test Case Prioritization Techniques Based on Weight Factors

Is This a Bug or an Obsolete Test?

Coverage-Based Clustering and Scheduling Approach for Test Case Prioritization

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