Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Constructing universal version history
Proceedings of the 2006 international workshop on Mining software repositories
Large-Scale Code Reuse in Open Source Software
FLOSS '07 Proceedings of the First International Workshop on Emerging Trends in FLOSS Research and Development
Succession: Measuring transfer of code and developer productivity
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Test coverage and post-verification defects: A multiple case study
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Software Dependencies, Work Dependencies, and Their Impact on Failures
IEEE Transactions on Software Engineering
Software support tools and experimental work
Proceedings of the 2006 international conference on Empirical software engineering issues: critical assessment and future directions
Assessing the state of software in a large enterprise
Empirical Software Engineering
Organizational volatility and its effects on software defects
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
High-impact defects: a study of breakage and surprise defects
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
A Large-Scale Empirical Study of Just-in-Time Quality Assurance
IEEE Transactions on Software Engineering
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As the development of software products frequently transitions among globally distributed teams, the knowledge about the source code, design decisions, original requirements, and the history of troublesome areas gets lost. A new team faces tremendous challenges to regain that knowledge. In numerous projects we observed that only 1% of project files are involved in more than 60% of the customer reported defects (CFDs), thus focusing quality improvement on such files can greatly reduce the risk of poor product quality. We describe a mostly automated approach that annotates the source code at the file and module level with the historic information from multiple version control, issue tracking, and an organization's directory systems. Risk factors (e.g, past changes and authors who left the project) are identified via a regression model and the riskiest areas undergo a structured evaluation by experts. The results are presented via a web-based tool and project experts are then trained how to use the tool in conjunction with a checklist to determine risk remediation actions for each risky file. We have deployed the approach in seven projects in Avaya and are continuing deployment to the remaining projects as we are evaluating the results of earlier deployments. The approach is particularly helpful to focus quality improvement effort for new releases of deployed products in a resource-constrained environment.