How Can I Use This Method?

Moreno, Laura; Bavota, Gabriele; Penta, Massimiliano Di; Oliveto, Rocco; Marcus, Andrian

doi:10.1109/icse.2015.98

“…Sourcerer [4]'s team did not reply about the use of their SAS code search engine. Muse [35] is not relevant: -focuses on API -cannot be queried for snippets. SNIFF [10] engine could not work (issue with the Eclipse plugin).…”

Section: Discussionmentioning

confidence: 99%

“…Note that state-of-the-art approaches in the literature, such as Muse [35] and MAPO [44], focus on finding usage examples of API methods whose names must be explicitly indicated in the query. Thus, they may not be suitable for development tasks where users do not know the source code keywords of the relevant APIs.…”

Section: Limitations Of the State-of-the-artmentioning

confidence: 99%

Augmenting and structuring user queries to support efficient free-form code search

Sirres

¹

,

Bissyandé

²

,

Kim

³

et al. 2018

View full text Add to dashboard Cite

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and Stack Overflow Q&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users complete solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for Stack Overflow questions.

show abstract

“…Recently, there have been been a number of code search techniques [6,19,25,31,35,43], focusing on locating API usage examples. Searching for specific API usages is a subset of code search activities.…”

Section: Api Usage Examples Searchmentioning

confidence: 99%

“…Internal validity: the user study was performed with a limited total number of 34 (=14+20) participants compared to the large number of participants used by Muse [35] authors for their API example search engine. However, among free-form code search works, some do not perform user studies (e.g., [4]), while others use fewer participants than us (e.g., CodeHow (20), Portfolio (19), SNIFF (undisclosed)).…”

Section: Threats To Validitymentioning

confidence: 99%

Augmenting and structuring user queries to support efficient free-form code search

Sirres

¹

,

Bissyandé

²

,

Kim

³

et al. 2018

Proceedings of the 40th International Conference on Software Engineering

View full text Add to dashboard Cite

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and Stack Overflow Q&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users complete solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for Stack Overflow questions.

show abstract

“…Whilst that list covers the more common applications, similarity assessment is used in many other areas, too. Examples include finding similar bug fixes (Hartmann et al 2010), identifying cross-cutting concerns (Bruntink et al 2005), program comprehension (Maletic and Marcus 2001), code recommendation (Holmes and Murphy 2005), and example extraction (Moreno et al 2015).…”

Section: Introductionmentioning

confidence: 99%

A comparison of code similarity analysers

Ragkhitwetsagul

¹

,

Krinke

²

,

Clark

³

2017

View full text Add to dashboard Cite

Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code.

show abstract

How Can I Use This Method?

Cited by 77 publications

References 13 publications

Augmenting and structuring user queries to support efficient free-form code search

Augmenting and structuring user queries to support efficient free-form code search

Augmenting and structuring user queries to support efficient free-form code search

A comparison of code similarity analysers

Contact Info

Product

Resources

About