Source Code Exploration with Google

Poshyvanyk, Denys; Petrenko, Maksym; Marcus, Andrian; Xie, Xia; Liu, Dapeng

doi:10.1109/icsm.2006.60

Cited by 44 publications

(44 citation statements)

References 8 publications

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“…The method was later extended by using formal concept analysis to cluster the results of the search [22]. A different implementation of the method was done using Google Desktop Search [23] as the underlying IR engine. More recently, Lukins at al.…”

Section: Related Workmentioning

confidence: 99%

On the use of relevance feedback in IR-based concept location

Haiduc

Marcus

Menzies

2009

2009 IEEE International Conference on Software Maintenance

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143

107

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Section: Related Workmentioning

confidence: 99%

On the use of relevance feedback in IR-based concept location

Haiduc

Marcus

Menzies

2009

2009 IEEE International Conference on Software Maintenance

Self Cite

143

107

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“…The closely related work [50,51] infers semantically related words in software, and leverages them to build a search tool that outperforms two existing approaches [42]. We have already discussed the main differences between the previous work and our context-based approach in Chapter 1, so we only summarize them here and provide more examples.…”

Section: Extracting Semantically Related Word Pairsmentioning

confidence: 99%

“…Keyword-based code search techniques have been developed [16,18,19,41,42,50]. Since our technique infers semantically related words from software, it can be leveraged by these search tools to expand queries to further improve the search effectiveness.…”

Section: Code Searchmentioning

confidence: 99%

See 1 more Smart Citation

SWordNet: Inferring semantically related words from software context

Yang

Tan

2013

Empir Software Eng

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Code search is an integral part of software development and program comprehension. The difficulty of code search lies in the inability to guess the exact words used in the code. Therefore, it is crucial for keyword-based code search to expand queries with semantically related words, e.g., synonyms and abbreviations, to increase the search effectiveness. However, it is limited to rely on resources such as English dictionaries and WordNet to obtain semantically related words in software, because many words that are semantically related in software are not semantically related in English. On the other hand, many words that are semantically related in English are not semantically related in software.This thesis proposes a simple and general technique to automatically infer semantically related words (referred to as rPairs) in software by leveraging the context of words in comments and code. In addition, we propose a ranking algorithm on the rPair results and study cross-project rPairs on two sets of software with similar functionality, i.e., media browsers and operating systems. We achieve a reasonable accuracy in nine large and popular code bases written in C and Java. Our further evaluation against the state of art shows that our technique can achieve a higher precision and recall. In addition, the proposed ranking algorithm improves the rPair extraction accuracy by bringing correct rPairs to the top of the list. Our cross-project study successfully discovers overlapping rPairs among projects of similar functionality and finds that cross-project rPairs are more likely to be correct than project-specific rPairs. Since the cross-project rPairs are highly likely to be general for software of the same type, the discovered overlapping rPairs can benefit other projects of the same type that have not been anaylyzed.iii Acknowledgements I would like to take this opportunity to express my biggest thanks to my supervisor Prof. Lin Tan. From her, I get numerous supports and encouragement in every aspect. I would like to thank for her insightful guidance and unwavering supports. She is always available when I ask for help or advice. I learned a lot from Lin, including conducting research, time management, presentations skills and how to communicate and collaborate with other people. The things I learned from Lin will extremely benefit my future development. I am thankful to readers of the thesis, Prof. Ladan Tahvildari and Prof. Mahesh V. Tripunitara, for spending their valuable time in reviewing my thesis and providing valuable comments.

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“…Several refereed publications resulted from this research [14,18,19,26,27,[29][30][31] in addition to the dissertation [23].…”

Section: Publications and Current Workmentioning

confidence: 99%