Incorporating latent semantic indexing into a neural network model for information retrieval
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
A vector space model for automatic indexing
Communications of the ACM
Software Change Impact Analysis
Software Change Impact Analysis
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Hipikat: recommending pertinent software development artifacts
Proceedings of the 25th International Conference on Software Engineering
Detection of Logical Coupling Based on Product Release History
ICSM '98 Proceedings of the International Conference on Software Maintenance
Mining Version Histories to Guide Software Changes
Proceedings of the 26th International Conference on Software Engineering
NavTracks: Supporting Navigation in Software Maintenance
ICSM '05 Proceedings of the 21st IEEE International Conference on Software Maintenance
Object-Oriented Software Engineering: Using UML, Patterns and Java, Second Edition
Object-Oriented Software Engineering: Using UML, Patterns and Java, Second Edition
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The central problem addressed by this interdisciplinary paper is to predict related software artifacts that are usually changed together by a developer. The working focus of programmers is revealed by means of their interactions with a software repository that receives a set of cohesive artifact changes within one commit transaction. This implicit knowledge of interdependent changes can be exploited in order to recommend likely further changes, given a set of already changed artifacts. We suggest a hybrid approach based on Latent Semantic Indexing(LSI) and machine learning methods to recommend software development artifacts, that is predicting a sequence of configuration items that were committed together. As opposed to related approaches to repository mining that are mostly based on symbolic methods like Association Rule Mining(ARM), our connectionist method is able to generalize onto unseen artifacts. Text analysis methods are employed to consider their textual attributes. We applied our technique to three publicly available datasets from the PROMISE Repository of Software Engineering Databases. The evaluation showed that the connectionist LSI-approach achieves a significantly higher recommendation accuracy than existing methods based on ARM. Even when generalizing onto unseen artifacts, our approach still provides an accuracy of up to 72.7% on the given datasets.