Software Release Assessment under Multiple Alternatives with Consideration of Debuggers’ Learning Rate and Imperfect Debugging Environment

Qing Tian, Chih-Chiang Fang and Chun-Wu Yeh
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Qing Tian: School of Computer Science and Software, Zhaoqing University, Zhaoqing 526061, China
Chih-Chiang Fang: School of Computer Science and Software, Zhaoqing University, Zhaoqing 526061, China
Chun-Wu Yeh: Computer and Game Development Program & Department of Information Management, Kun Shan University, Tainan 710303, Taiwan

Mathematics, 2022, vol. 10, issue 10, 1-24

Abstract: In the software development life cycle, the quality and reliability of software are critical to software developers. Poor quality and reliability not only cause the loss of customers and sales but also increase the operational risk due to unreliable codes. Therefore, software developers should try their best to reduce such potential software defects by undertaking a software testing project. However, to pursue perfect and faultless software is unrealistic since the budget, time, and testing resources are limited, and the software developers need to reach a compromise that balances software reliability and the testing cost. Using the model presented in this study, software developers can devise multiple alternatives for a software testing project, and each alternative has its distinct allocation of human resources. The best alternative can therefore be selected. Furthermore, the allocation incorporates debuggers’ learning and negligent factors, both of which influence the efficiency of software testing in practice. Accordingly, the study considers both human factors and the nature of errors during the debugging process to develop a software reliability growth model to estimate the related costs and the reliability indicator. Additionally, the issue of error classification is also extended by considering the impacts of errors on the system, and the expected time required to remove simple or complex errors can be estimated based on different truncated exponential distributions. Finally, numerical examples are presented and sensitivity analyses are performed to provide managerial insights and useful directions to inform software release strategies.

Keywords: software reliability; reliability growth; learning effect; imperfect debugging; Nonhomogeneous Poisson Process (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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