Article detail · 2009
Practical considerations in deploying AI for defect prediction
- Year
- 2009
- Type
- conference-paper
Abstract
OpenAlex · English
We have conducted a study in a large telecommunication company in Turkey to employ a software measurement program and to predict pre-release defects. We have previously built such predictors using AI techniques. This project is a transfer of our research experience into a real life setting to solve a specific problem for the company: to improve code quality by predicting pre-release defects and efficiently allocating testing resources. Our results in this project have many practical implications that managers have started benefiting: code analysis, bug tracking, effective use of version management system and defect prediction. Using version history information, developers can find around 88% of the defects with 28% false alarms, compared to same detection rate with 50% false alarms without using historical data. In this paper we also shared in detail our experience in terms of the project steps (i.e. challenges and opportunities).
Topics
Citations
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63 citations
OpenAlex cited_by_count (cache / database)
14 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
- Practical considerations in deploying statistical methods for defect prediction A case study within the Turkish telecommunications industry 2010
- Practical considerations in deploying statistical methods for defect prediction: A case study within the Turkish telecommunications industry 2010
- An industrial case study of classifier ensembles for locating software defects 2011
- AI‐Based Software Defect Predictors: Applications and Benefits in a Case Study 2011
- An industrial case study of classifier ensembles for locating software defects 2011
- Influence of confirmation biases of developers on software quality: an empirical study 2012
- Defect prediction using social network analysis on issue repositories 2011
- Predicting defective modules in different test phases 2015
- Predicting defective modules in different test phases 2014