Whole program Path-Based dynamic impact analysis
Proceedings of the 25th International Conference on Software Engineering
Leveraging field data for impact analysis and regression testing
Proceedings of the 9th European software engineering conference held jointly with 11th ACM SIGSOFT international symposium on Foundations of software engineering
Using Development History Sticky Notes to Understand Software Architecture
IWPC '04 Proceedings of the 12th IEEE International Workshop on Program Comprehension
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Fine-grained processing of CVS archives with APFEL
eclipse '06 Proceedings of the 2006 OOPSLA workshop on eclipse technology eXchange
Automatic Inference of Structural Changes for Matching across Program Versions
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Change Distilling: Tree Differencing for Fine-Grained Source Code Change Extraction
IEEE Transactions on Software Engineering
Enabling static analysis for partial java programs
Proceedings of the 23rd ACM SIGPLAN conference on Object-oriented programming systems languages and applications
What is the long-term impact of changes?
Proceedings of the 2008 international workshop on Recommendation systems for software engineering
Change impact graphs: Determining the impact of prior codechanges
Information and Software Technology
Discovering Patterns of Change Types
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Testing principles, current practices, and effects of change localization
Proceedings of the 10th Working Conference on Mining Software Repositories
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Developers change source code to add new functionality, fix bugs, or refactor their code. Many of these changes have immediate impact on quality or stability. However, some impact of changes may become evident only in the long term. The goal of this thesis is to explore the long-term impact of changes by detecting dependencies between code changes and by measuring their influence on software quality, software maintainability, and development effort. Being able to identify the changes with the greatest long-term impact will strengthen our understanding of a project's history and thus shape future code changes and decisions.