Article detail · 2011
AI‐Based Software Defect Predictors: Applications and Benefits in a Case Study
Journal
AI Magazine- Year
- 2011
- Type
- article
Data source split
- YÖKSİS venue AI Magazine
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Software defect prediction aims to reduce software testing efforts by guiding testers through the defect‐prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real‐life setting is difficult because it requires software metrics and defect data from past projects to predict the defect‐proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time, and reduces the testing effort. We have built a learning‐based defect prediction model for a telecommunications company in the space of one year. In this study, we have briefly explained our model, presented its payoff, and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called team software process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent, and hence reduce postrelease defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
67 citations
OpenAlex cited_by_count (cache / database)
11 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Defect prediction from static code features: current results, limitations, new approaches 2010
- Multiple-classifiers in software quality engineering: Combining predictors to improve software fault prediction ability 2020
- Predicting defective modules in different test phases 2015
- Predicting defective modules in different test phases 2014
- A Retrospective Study of Software Analytics Projects In Depth Interviews with Practitioners 2013
- A Retrospective Study of Software Analytics Projects: In-Depth Interviews with Practitioners 2013
- Lessons Learned from Software Analytics in Practice 2015
- Guest editorial: learning to organize testing 2011
- Handling missing attributes using matrix factorization 2013
- A success story in applying data science in practice 2016