Bayesian networks for evidence-based decision-making in software engineering

Ayse Tosun Misirli, Ayse Basar Bener

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)

Abstract

Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). In this study, we follow the lead of computational biology and healthcare decision-making, and investigate the applications of BNs in SE in terms of 1) main software engineering challenges addressed, 2) techniques used to learn causal relationships among variables, 3) techniques used to infer the parameters, and 4) variable types used as BN nodes. We conduct a systematic mapping study to investigate each of these four facets and compare the current usage of BNs in SE with these two domains. Subsequently, we highlight the main limitations of the usage of BNs in SE and propose a Hybrid BN to improve evidence-based decision-making in SE. In two industrial cases, we build sample hybrid BNs and evaluate their performance. The results of our empirical analyses show that hybrid BNs are powerful frameworks that combine expert knowledge with quantitative data. As researchers in SE become more aware of the underlying dynamics of BNs, the proposed models will also advance and naturally contribute to evidence based-decision-making.

Original languageEnglish
Article number6808495
Pages (from-to)533-554
Number of pages22
JournalIEEE Transactions on Software Engineering
Volume40
Issue number6
DOIs
Publication statusPublished - 1 Jun 2014
Externally publishedYes

Keywords

  • Bayesian networks
  • Bayesian statistics
  • Evidence-based decision-making
  • post-release defects
  • software metrics
  • software reliability

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