Abstract
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).
Original language | English |
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Title of host publication | PROMISE 2009 - International Conference on Predictor Models in Software Engineering |
DOIs | |
Publication status | Published - 1 Dec 2009 |
Externally published | Yes |
Event | 5th International Conference on Predictor Models in Software Engineering, PROMISE '09 - Vancouver, BC, Canada Duration: 18 May 2009 → 19 May 2009 |
Conference
Conference | 5th International Conference on Predictor Models in Software Engineering, PROMISE '09 |
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Country/Territory | Canada |
City | Vancouver, BC |
Period | 18/05/09 → 19/05/09 |
Keywords
- AI methods
- experience report
- prediction
- software defect prediction
- static code attributes