Abstract
This paper focuses on an exciting and essential problem in software companies. The software life cycle includes testing software, which is often time-consuming, and is a critical phase in the software development process. To reduce time spent on testing and to maintain software quality, the idea of a systematic selection of test cases is needed. Attracted by the claim, researchers presented test case prioritization (TCP) by applying the concepts of multi-criteria decision-making (MCDM). However, the literature on TCP suffers from the following issues: (i) difficulty in properly handling uncertainty; (ii) systematic evaluation of criteria by understanding the hesitation of experts; and (iii) rational prioritization of test cases by considering the nature of criteria. Motivated by these issues, an integrated approach is put forward that could circumvent the problem in this paper. The main aim of this research is to develop a decision model with integrated methods for TCP. The core importance of the proposed model is to (i) provide a systematic/methodical decision on TCP with a reduction in testing time and cost; (ii) help software personnel choose an apt test case from the suite for testing software; (iii) reduce human bias by mitigating intervention of personnel in the decision process. To this end, probabilistic linguistic information (PLI) is adopted as the preference structure that could flexibly handle uncertainty by associating occurrence probability to each linguistic term. Furthermore, an attitude-based entropy measure is presented for criteria weight calculation, and finally, the EDAS ranking method is extended to PLI for TCP. An empirical study of TCP in a software company is presented to certify the integrated approach’s effectiveness. The strengths and weaknesses of the introduced approach are conferred by comparing it with the relevant methods.
Highlights
Multi-criteria decision-making (MCDM) is an attractive concept that involves a set of options selected based on a set of criteria, either linguistically or numerically
Rodriguez et al [4] identified a crucial weakness of linguistic term sets (LTSs) and proposed hesitant fuzzy linguistic term sets (HFLTSs) to resolve the same
From the detailed survey prepared by Liao et al [16], it is evident that (i) probabilistic linguistic information (PLI) is a strong structure for handling uncertainty, and (ii) test case prioritization (TCP) is not explored under the PLI context
Summary
Multi-criteria decision-making (MCDM) is an attractive concept that involves a set of options selected based on a set of criteria, either linguistically or numerically. The core strength of HFLTSs is that they combine the ability of LTSs and hesitant fuzzy sets (HFSs), [5,6] which allows for preference elicitation linguistically, and offers flexibility to handle hesitation better. Attracted by this flexibility, many researchers contribute MCDM methods under HFLTSs [7]. From the detailed survey prepared by Liao et al [16], it is evident that (i) PLI is a strong structure for handling uncertainty, and (ii) TCP is not explored under the PLI context
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