TY - GEN
T1 - Practical considerations in deploying AI for defect prediction
T2 - 5th International Conference on Predictor Models in Software Engineering, PROMISE '09
AU - Tosun, Ayşe
AU - Turhan, Burak
AU - Bener, Ayşe
PY - 2009
Y1 - 2009
N2 - 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).
AB - 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).
KW - AI methods
KW - experience report
KW - prediction
KW - software defect prediction
KW - static code attributes
UR - http://www.scopus.com/inward/record.url?scp=77953860933&partnerID=8YFLogxK
U2 - 10.1145/1540438.1540453
DO - 10.1145/1540438.1540453
M3 - Conference contribution
AN - SCOPUS:77953860933
SN - 9781605586342
T3 - ACM International Conference Proceeding Series
BT - PROMISE 2009 - International Conference on Predictor Models in Software Engineering
Y2 - 18 May 2009 through 19 May 2009
ER -