DIV‐TBAT algorithm for test suite reduction in software testing

Abstract

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.

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

“…In this work, we found and evaluated 23 studies that focussed on swarm intelligence-based TSR approaches. It includes some meta-heuristic algorithms, such as ACO [9], firefly algorithm (FA) [55], flower pollination algorithm (FPA) [56,57], and bat algorithm [58] used for TSR. Table 9 presents the data extracted from the selected studies.…”

“…However, their proposed approach lacks finding the defined best value for the switch probability necessary for TSR. In contrast, Sugave et al [58] developed two approaches for TSR. In the first approach, the authors devised an ATAP measure TSR based on FPA called TGFP.…”

“…� Percentage Test Time Reduction (PTTR): This parameter determines the effectiveness of a TSR approach based on the required computation time for RS and is mathematically computed using the following formula [58]: where Time (TS) represents the time taken by the original test suite, while Time (RS) represents the time taken by RS. � FDC: It determines the effectiveness of a search-based TSR approach based on FDC of the determined RS [2,35] and is represented as follows: The search algorithm's performance in terms of size reduction, time reduction, coverage achievement, and fault detection used in the selected studies is shown in Figure 10.…”

“…In contrast, Sugave et al. [58] developed two approaches for TSR. In the first approach, the authors devised an ATAP measure TSR based on FPA called TGFP.…”

“…In the literature, the following effectiveness measures are used to evaluate the effectiveness of the proposed search‐based TSR approaches: Percentage Test Suite Reduction (PTSR): Generally, the effectiveness of a search‐based TSR approach is computed using the PTSR measure. It determines the size of the RS compared to the original test cases and is formally represented as [2, 41]follows: where RS represents the optimised test suite, and TS denotes the original test suite's size. Percentage Test Time Reduction (PTTR): This parameter determines the effectiveness of a TSR approach based on the required computation time for RS and is mathematically computed using the following formula [58]: where Time (TS) represents the time taken by the original test suite, while Time (RS) represents the time taken by RS. Percentage of Coverage Achieved (PCOA): It determines the number of elements covered by the optimised test case and is formally represented as [39, 98 follows]: FDC: It determines the effectiveness of a search‐based TSR approach based on FDC of the determined RS [2, 35] and is represented as follows: …”