A study of software reliability growth from the perspective of learning effects

Kuei Chen Chiu, Yeu Shiang Huang, Tzai Zang Lee

Research output: Contribution to journalArticlepeer-review

123 Citations (Scopus)


For the last three decades, reliability growth has been studied to predict software reliability in the testing/debugging phase. Most of the models developed were based on the non-homogeneous Poisson process (NHPP), and S-shaped type or exponential-shaped type of behavior is usually assumed. Unfortunately, such models may be suitable only for particular software failure data, thus narrowing the scope of applications. Therefore, from the perspective of learning effects that can influence the process of software reliability growth, we considered that efficiency in testing/debugging concerned not only the ability of the testing staff but also the learning effect that comes from inspecting the testing/debugging codes. The proposed approach can reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and the results in the experiment show good fit. A comparative analysis to evaluate the effectiveness for the proposed model and other software failure models was also performed. Finally, an optimal software release policy is suggested.

Original languageEnglish
Pages (from-to)1410-1421
Number of pages12
JournalReliability Engineering and System Safety
Issue number10
Publication statusPublished - 2008 Oct 1

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality


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