An Empirical Comparison of Dynamic Impact Analysis Algorithms
Proceedings of the 26th International Conference on Software Engineering
Efficient and precise dynamic impact analysis using execute-after sequences
Proceedings of the 27th international conference on Software engineering
Towards a more efficient static software change impact analysis method
Proceedings of the 8th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
A holistic approach to managing software change impact
Journal of Systems and Software
Impact Analysis using Class Interaction Prediction Approach
Proceedings of the 2010 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the 9th SoMeT_10
A taxonomy for software change impact analysis
Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution
A practice-driven systematic review of dependency analysis solutions
Empirical Software Engineering
Maintaining the health of software monitors
Innovations in Systems and Software Engineering
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Impact analysis - determining the potential effects ofchanges on a software system - plays an important role inhelping engineers re-validate modified software. In previouswork we presented a new impact analysis technique,PathImpact, for performing dynamic impact analysis atthe level of procedures, and we showed empirically thatthe technique can be cost-effective in comparison to prominentprior techniques. A drawback of that approach as presented,however, is that when attempting to apply the techniqueto a new version of a system as that system and itstest suite evolves, the process of recomputing the data requiredby the technique for that version can be excessivelyexpensive. In this paper, therefore, we present algorithmsthat allow the data needed by PathImpact to be collectedincrementally. We present the results of a controlled experimentinvestigating the costs and benefits of this incrementalapproach relative to the approach of completely recomputingprerequisite data.