Practical considerations in deploying AI for defect prediction: A case study within the Turkish telecommunication industry

Ayşe Tosun, Burak Turhan, Ayşe Bener

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

21 Citations (Scopus)

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 languageEnglish
Title of host publicationPROMISE 2009 - International Conference on Predictor Models in Software Engineering
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event5th International Conference on Predictor Models in Software Engineering, PROMISE '09 - Vancouver, BC, Canada
Duration: 18 May 200919 May 2009

Conference

Conference5th International Conference on Predictor Models in Software Engineering, PROMISE '09
CountryCanada
CityVancouver, BC
Period18/05/0919/05/09

Keywords

  • AI methods
  • experience report
  • prediction
  • software defect prediction
  • static code attributes

Cite this

Tosun, A., Turhan, B., & Bener, A. (2009). Practical considerations in deploying AI for defect prediction: A case study within the Turkish telecommunication industry. In PROMISE 2009 - International Conference on Predictor Models in Software Engineering [1540453] https://doi.org/10.1145/1540438.1540453