Software component recommendation using collaborative filtering

Ichii, Makoto; Hayase, Yasuhiro; Yokomori, Reishi; Yamamoto, Takakazu; Inoue, Katsuro

doi:10.1109/suite.2009.5070014

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…This is in accordance with empirical findings on open source that have shown how cross-project developers are a good indicator of project success [8]. This behaviour is also known as "the rich get richer" in the theory of scale-free networks and is 7 "term frequency-inverse document frequency" http://en. wikipedia.org/wiki/Tf-idf considered an inherent and thus common property of most social networks [3].…”

Section: Trustability Metricsupporting

confidence: 89%

A trustability metric for code search based on developer karma

Gysin

Kuhn

2010

Proceedings of 2010 ICSE Workshop on Search-Driven Development: Users, Infrastructure, Tools and Evaluation

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The promise of search-driven development is that developers will save time and resources by reusing external code in their local projects. To efficiently integrate this code, users must be able to trust it, thus trustability of code search results is just as important as their relevance. In this paper, we introduce a trustability metric to help users assess the quality of code search results and therefore ease the cost-benefit analysis they undertake trying to find suitable integration candidates. The proposed trustability metric incorporates both user votes and cross-project activity of developers to calculate a "karma" value for each developer. Through the karma value of all its developers a project is ranked on a trustability scale. We present JBender , a proof-of-concept code search engine which implements our trustability metric and we discuss preliminary results from an evaluation of the prototype.

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Section: Trustability Metricsupporting

confidence: 89%

A trustability metric for code search based on developer karma

Gysin

Kuhn

2010

Proceedings of 2010 ICSE Workshop on Search-Driven Development: Users, Infrastructure, Tools and Evaluation

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“…Among existing code recommendation techniques, Ichii et al [52] seem to allow opportunistic reuse by using collaborative filtering to help developers find components suitable for their needs. This system extracts a developer's browsing history when the developer starts navigating through the search results provided by a SPARS-J [53] search engine.…”

Section: Code Recommendation Systemsmentioning

confidence: 99%

FACER: An API Usage-based Code-example Recommender for Opportunistic Reuse

Abid

Shamail

Basit

et al. 2021

Preprint

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To save time, developers often search for code examples that implement their desired software features. Existing code search techniques typically focus on ﬁnding code snippets for a single given query, which means that developers need to perform a separate search for each desired functionality. In this paper, we pro-pose FACER (Feature-driven API usage-based Code Examples Recommender), a technique that avoids repeated searches through opportunistic reuse. Speciﬁcally, given the selected code snippet that matches the initial search query, FACER ﬁnds and suggests related code snippets that represent features that the developer may want to implement next. FACER ﬁrst constructs a code fact repository by parsing the source code of open-source Java projects to obtain methods’ textual information, call graphs, and Application Programming Interface (API) usages. It then detects unique features by clustering methods based on similar API us-ages, where each cluster represents a feature or functionality. Finally, it detects frequently co-occurring features across projects using frequent pattern mining and recommends related methods from the mined patterns. To evaluate FACER, we run it on 120 Java Android apps from GitHub. We ﬁrst manually validate that the detected method clusters represent methods with similar functionality. We then perform an automated evaluation to determine the best parameters (e.g., similarity threshold) for FACER. We recruit 10 professional developers along with 39 experienced students to judge FACER’s recommendation of related methods. Our results show that, on average, FACER’s recommendations are 80% precise. We also survey a total of 20 professional Android and Java developers to understand their code search and reuse experiences, and also to obtain their feedback on the usability and usefulness of FACER. The survey results show that 95% of our surveyed professional developers ﬁnd the idea of related method recommendations useful during code reuse.

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“…1) Recommender systems (Bhaskar, Claudia, Avare, Claudio, & J., 2004;Birukou et al, 2007;Blake & Nowlan, 2007;Ichii, Hayase, Yokomori, Yamamoto, & Inoue, 2009;Sellami, Tata, Maamar, & Defude, 2009;Shripad & V., 2005;Zheng, Ma, Lyu, & King, 2009): These systems try to discover the most likely web services based on the behaviour of other users and by preserving the system history.…”

Section: Related Workmentioning

confidence: 99%

“…Ichii et al (2009) present a recommender system based on collaborative filtering to propose software components. However, Zheng et al (2009) propose a collaborative filtering system for web service recommendations based on QoS.…”

Section: Related Workmentioning

confidence: 99%

“…However, Zheng et al (2009) propose a collaborative filtering system for web service recommendations based on QoS. Both presented methods in (Ichii et al, 2009;Zheng et al, 2009) utilise the history of user activity and present a method for expressing the similarity between users. Blake and Nowlan (2007) present a method for web service discovery and candidate recommendation based on syntactical matching.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

WSDATool: A Novel Web Service Developer Assistance Tool Using a New Complementary Service Publishing Method

Abdali-Mohammadi¹,

Nematbakhsh²,

Nematbakhsh³

2012

MAS

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Web service technology (WST) is a service-oriented architecture implementation framework that makes designing component-based internet applications possible. At present, many providers offer their services as web services. Current WST suffers from the lack of an integrated tool to assist web service developers. In WST, the services are published publicly, and their descriptions are stored in service directories. These descriptions contain valuable information about the work of different software teams throughout the world. However, with the increasing number of web services, searching for services is difficult and time-consuming. Furthermore, in current service directories, there is a little knowledge about the services, and extraction of useful information to be utilised by developers is not easy. In this paper, in order to increase the knowledge of what is available in service directories, a structure is presented by interlinking WST entities by using some defined semantic relations. The proposed structure provides a framework and a tool named WSDATool to develop new web services using information from published services or to refine current published web service descriptions. In experiments, services designed with the assistance of the WSDATool are more coherent and well designed.

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Software component recommendation using collaborative filtering

Cited by 11 publications

References 5 publications

A trustability metric for code search based on developer karma

A trustability metric for code search based on developer karma

FACER: An API Usage-based Code-example Recommender for Opportunistic Reuse

WSDATool: A Novel Web Service Developer Assistance Tool Using a New Complementary Service Publishing Method

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