Similarity-based regression test case prioritization

Wang, Rongcun; Jiang, Shujuan; Deng, Changhong

doi:10.18293/seke2015-115

Cited by 12 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarity-based algorithms have also been applied to regression test case prioritization, based on the distances between pair-wise test cases (Wang et al 2015), using historical failure data (Noor and Hemmati 2015) or topics models (Thomas et al 2014). Research effort have also been devoted to similarity-based selection techniques, in particular, in the model-based domain (Hemmati et al 2013).…”

Section: Previous Workmentioning

confidence: 99%

Similarity-based prioritization of test case automation

et al. 2018

View full text Add to dashboard Cite

The importance of efficient software testing procedures is driven by an ever increasing system complexity as well as global competition. In the particular case of manual test cases at the system integration level, where thousands of test cases may be executed before release, time must be well spent in order to test the system as completely and as efficiently as possible. Automating a subset of the manual test cases, i.e, translating the manual instructions to automatically executable code, is one way of decreasing the test effort. It is further common that test cases exhibit similarities, which can be exploited through reuse when automating a test suite. In this paper, we investigate the potential for reducing test effort by ordering the test cases before such automation, given that we can reuse already automated parts of test cases. In our analysis, we investigate several approaches for prioritization in a case study at a large Swedish vehicular manufacturer. The study analyzes the effects with respect to test effort, on four projects with a total of 3919 integration test cases constituting 35,180 test steps, written in natural language. The results show that for the four projects considered, the difference in expected manual effort between the best and the worst order found is on average 12 percentage points. The results also show that our proposed prioritization method is nearly as good as more resource demanding meta-heuristic approaches Software Qual J at a fraction of the computational time. Based on our results, we conclude that the order of automation is important when the set of test cases contain similar steps (instructions) that cannot be removed, but are possible to reuse. More precisely, the order is important with respect to how quickly the manual test execution effort decreases for a set of test cases that are being automated.

show abstract

Section: Previous Workmentioning

confidence: 99%

Similarity-based prioritization of test case automation

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Ripon et al [19] introduce a REPiR approach by reducing the regression test prioritization to a standard Information Retrieval problem such that the differences between two program versions form the query and the tests constitute the document collection. Wang et al [20] propose a global similarity-based regression test case prioritization approach based on the distance between pair-wise test cases.…”

Section: Related Workmentioning

confidence: 99%

Coverage-Based Clustering and Scheduling Approach for Test Case Prioritization

Fan

et al. 2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

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.

show abstract

“…To address this problem, a number of techniques and framework have been proposed to make the reduction process more effective which is based on test case classification according to similarity degree measured by a distance function [2,[15][16]. Diversity and similarity-based test case selection and prioritization are one of the new approaches with favourable output [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Chunrong et al [27] proposed different similarity based prioritization techniques to order the test cases according to their importance with the help of edit distances. Wang et al [19] proposed global similarity based prioritization to overcome the problems that are not effectively solved by coverage based prioritization techniques. All the above techniques have their common objective i.e.…”

Section: Introductionmentioning

confidence: 99%

A New Similarity-Based Greedy Approach for Generating Effective Test Suite

Singh¹,

Shree²

2018

IJIES

View full text Add to dashboard Cite

Software regression testing is one of the most critical phases of software development life cycle, used by developers with the intent of detecting new faults to validate modified software prior to delivery to the customer. To validate updated features, new test cases are generated by the testers which increment the test suite size automatically. The resulting test suite may contain obsolete, redundant, and ambiguous test cases. Therefore, there is a strong requirement of an intelligent testing approach to reduce the test suite size by removing those unessential test cases economically. This paper proposed an interesting approach, which involves the combination of regression testing techniques: minimization, and prioritization both. The main focus is on multiple regression activities with multiple criteria rather than using only single activity to produce an optimal solution. In this paper clustering approach is also considered, which could simplify and enhance the minimization and prioritization task. To evaluate the effectiveness of the strategy, we performed an experimental investigation together with an eminent heuristic Harrold Gupta and Soffa (HGS), considering the testing measures of the minimized test suite size and fault coverage. The results show that, similarity-based greedy approach with multiple coverage criteria can be quite effective in terms of fault detection loss of reduced test suite without much affecting the percentage of suite size reduction.

show abstract

Similarity-based regression test case prioritization

Cited by 12 publications

References 22 publications

Similarity-based prioritization of test case automation

Similarity-based prioritization of test case automation

Coverage-Based Clustering and Scheduling Approach for Test Case Prioritization

A New Similarity-Based Greedy Approach for Generating Effective Test Suite

Contact Info

Product

Resources

About