A Cost-Aware Test Suite Minimization Approach Using TAP Measure and Greedy Search Algorithm

Sugave, Shounak Rushikesh; Patil, Suhas H.; Reddy, B. Eswara

doi:10.22266/ijies2017.0831.07

Cited by 4 publications

(12 citation statements)

References 22 publications

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“…In the first approach, the authors devised an ATAP measure TSR based on FPA called TGFP. They evaluated the proposed strategy performance with TBAT [64], greedy ratio [105], greedy EIrreplaceability [65], diversity-based GA [106], and TAP [106] for combinatorial testing. Based on the performed experiments, they reported that TGFP attained better performance in terms of test optimisation than the compared approaches.…”

Section: Swarm Intelligence-basedmentioning

confidence: 99%

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

2023

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Regression testing remains a promising research area for the last few decades. It is a type of testing that aims at ensuring that recent modifications have not adversely affected the software product. After the introduction of a new change in the system under test, the number of test cases significantly increases to handle the modification. Consequently, it becomes prohibitively expensive to execute all of the generated test cases within the allocated testing time and budget. To address this situation, the test suite reduction (TSR) technique is widely used that focusses on finding a representative test suite without compromising its effectiveness such as fault‐detection capability. In this work, a systematic review study is conducted that intends to provide an unbiased viewpoint about TSR based on various types of search algorithms. The study's main objective is to examine and classify the current state‐of‐the‐art approaches used in search‐based TSR contexts. To achieve this, a systematic review protocol is adopted and, the most relevant primary studies (57 out of 210) published between 2007 and 2022 are selected. Existing search‐based TSR approaches are classified into five main categories, including evolutionary‐based, swarm intelligence‐based, human‐based, physics‐based, and hybrid, grounded on the type of employed search algorithm. Moreover, the current work reports the parameter settings according to their category, the type of considered operator(s), and the probabilistic rate that significantly impacts on the quality of the obtained solution. Furthermore, this study describes the comparison baseline techniques that support the empirical comparison regarding the cost‐effectiveness of a search‐based TSR approach. Finally, it isconcluded that search‐based TSR has great potential to optimally solve the TSR problem. In this regard, several potential research directions are outlined as useful for future researchers interested in conducting research in the TSR domain.

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Section: Swarm Intelligence-basedmentioning

confidence: 99%

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

2023

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“…In future, we plan to verify the test suite reduction approaches for fault detection capability. Table 7 W-test on p-value (α = 0.01) and p-value (α = 0.1) DIV-TBAT versus GR [26] DIV-TBAT versus GE [24] DIV-TBAT versus DIV-GA [21] DIV-TBAT versus TAP [21] DIV-TBAT versus TBAT [22] DIV-TBAT versus ATAP DIV-TBAT versus GR [26] DIV-TBAT versus GE [24] DIV-TBAT versus DIV-GA [21] DIV-TBAT versus TAP [21] DIV-TBAT versus TBAT [22] DIV-TBAT versus ATAP…”

Section: Conclusion and Future Scopementioning

confidence: 99%

“…Summarising, the contributions of this paper are: i. New measure: A newly proposed measure called ATAP measure (Adaptive Test cases which is already included in Pool-based measure) which is the extension of the TAP measure given in [21]. ii.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we have developed a new measure called ATAP by modifying the TAP measure for test suite reduction. Summarising, the contributions of this paper are: New measure: A newly proposed measure called ATAP measure (Adaptive Test cases which is already included in Pool‐based measure) which is the extension of the TAP measure given in [21]. New objective functions: We have designed a new objective function for evaluating the test suite reduction.…”

Section: Introductionmentioning

confidence: 99%

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DIV‐TBAT algorithm for test suite reduction in software testing

2018

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Researchers have investigated different approaches to maintain the minimum cost and effort in regression testing. Here, test suite reduction is a common technique to decrease the cost of regression testing by removing the redundant test cases from the test suite and then, obtaining a representative set of test cases that still yield a high level of code coverage. Accordingly, here, the authors have developed two various techniques for test suite reduction. In the first technique, ATAP measure is newly developed to find the reduced test suite with the help of greedy search algorithm. In the second technique, DIV-TBAT (DIVersity-based BAT) algorithm is newly devised based on the mechanisms of Boolean logic within BAT algorithm which improve diversity during the search process. The proposed techniques are experimented using eight programs from SIR subject programs and the performance study is conducted using nine different evaluation metrics based on different research questions. The comparative analysis is performed with the existing algorithms like GreedyRatio, GreedyEIrreplaceability, diversity-based genetic algorithm, TBAT, and TAP, to prove the performance improvement over the eight software programs considered.

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“…Whereas, HGS approach is based on selection of test cases according to their degree of essentialness, i.e., the order of test cases is most essential to least essential in the reduced test suite. On the other hand, Shounak et al [20] proposed an algorithm i.e. GTAP) using TAP (Test cases which Already included in Pool-based Measure) measure and greedy search algorithm to reduce the number of test cases.…”

Section: Introductionmentioning

confidence: 99%

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

Singh¹,

Shree²

2018

IJIES

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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.

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A Cost-Aware Test Suite Minimization Approach Using TAP Measure and Greedy Search Algorithm

Cited by 4 publications

References 22 publications

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

DIV‐TBAT algorithm for test suite reduction in software testing

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

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