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Automatic generation of suggestions for program investigation
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Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering table of contents
Lisbon, Portugal
SESSION: Software change analysis table of contents
Pages: 11 - 20  
Year of Publication: 2005
ISBN:1-59593-014-0
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Author
Martin P. Robillard  McGill University, Montreal, QC, Canada
Sponsors
SIGSOFT: ACM Special Interest Group on Software Engineering
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 10,   Downloads (12 Months): 124,   Citation Count: 19
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ABSTRACT

Before performing a modification task, a developer usually has to investigate the source code of a system to understand how to carry out the task. Discovering the code relevant to a change task is costly because it is an inherently human activity whose success depends on a large number of unpredictable factors, such as intuition and luck. Although studies have shown that effective developers tend to explore a program by following structural dependencies, no methodology is available to guide their navigation through the typically hundreds of dependency paths found in a non-trivial program. In this paper, we propose a technique to automatically propose and rank program elements that are potentially interesting to a developer investigating source code. Our technique is based on an analysis of the topology of structural dependencies in a program. It takes as input a set of program elements of interest to a developer and produces a fuzzy set describing other elements of potential interest. Empirical evaluation of our technique indicates that it can help developers quickly select program elements worthy of investigation while avoiding less interesting ones.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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M. P. Robillard and G. C. Murphy. Automatically inferring concern code from program investigation activities. In Proceedings of the 18th International Conference on Automated Software Engineering, pages 225--234, 2003.
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CITED BY  19
 
 
 
 

Collaborative Colleagues:
Martin P. Robillard: colleagues