Part of the book series:Communications in Computer and Information Science ((CCIS,volume 432))
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Abstract
Due to the increasing of software requirements and software features, modern software systems continue to grow in size and complexity. Locating source code entities that required to implement a feature in millions lines of code is labor and cost intensive for developers. To this end, several studies have proposed the use of Information Retrieval (IR) to rank source code entities based on their textual similarity to an issue report. The ranked source code entities could be at a class or function granularity level. Source code entities at the class-level are usually large in size and might contain a lot of functions that are not implemented for the feature. Hence, we conjecture that the class-level feature location technique requires more effort than function-level feature location technique. In this paper, we investigate the impact of granularity levels on a feature location technique. We also presented a new evaluation method using effort-based evaluation. The results indicated that function-level feature location technique outperforms class-level feature location technique. Moreover, function-level feature location technique also required 7 times less effort than class-level feature location technique to localize the first relevant source code entity. Therefore, we conclude that feature location technique at the function-level of program elements is effective in practice.
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Authors and Affiliations
Software Engineering Laboratory, Graduate School of Information Science, Nara Institute of Science and Technology, Japan
Chakkrit Tantithamthavorn, Akinori Ihara, Hideaki Hata & Kenichi Matsumoto
- Chakkrit Tantithamthavorn
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- Akinori Ihara
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- Hideaki Hata
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- Kenichi Matsumoto
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Editors and Affiliations
Research Center for Human Centered Technology Design, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
Didar Zowghi
Key Laboratory of High Confidence Software Technologies (MoE), Peking University, Ministry of Education, 100871, Beijing, P.R. China
Zhi Jin
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Tantithamthavorn, C., Ihara, A., Hata, H., Matsumoto, K. (2014). Impact Analysis of Granularity Levels on Feature Location Technique. In: Zowghi, D., Jin, Z. (eds) Requirements Engineering. Communications in Computer and Information Science, vol 432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43610-3_11
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