Software defect prediction using Bayesian networks

By Ahmet Okutan, Olcay Taner Yildiz. Empirical Software Engineering, 2012

356 citations recorded by the source corpus.

There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/e73fe5f0c62a7a13f662435ca2bfc5c36d4e2d6d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Mohammad R. Alshayeb
2. Lahouari Ghouti
3. Issam Hadj Laradji

In Information Systems

1. Mohammad R. Alshayeb
2. Lahouari Ghouti
3. Issam Hadj Laradji

In Software

1. Mohammad R. Alshayeb
2. Lahouari Ghouti
3. Issam Hadj Laradji

Citations in the corpus

• Software defect prediction using ensemble learning on selected features (2015, methodology): “Using BN networks, Okutan and Yildiz [41] selected informative metrics/features by ranking them based on their correlation degree to defect-proneness.”
• An empirical study on software defect prediction with a simplified metric set (2014, neutral): “and determine the correct organization [41].”