A-SCORE: Automatic software component recommendation using coding context

Shimada, Ryuji; Hayase, Yasuhiro; Ichii, Makoto; Matsushita, Masayuki; Inoue, Katsuro

doi:10.1109/icse-companion.2009.5071051

Cited by 4 publications

(1 citation statement)

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“…CodeBroker uses comments and method signatures of a yet-to-be-written method to retrieve relevant code, whereas in Strathcona, the search query is either the structural information of some class or method (signature, object instantiations) that the developer needs help for. Others include: A-Score [49], which recommends a list of classes against user code based on cosine similarity of code characteristics; Selene [50], which forms a search query from the code around the user's cursor in an IDE and provides code examples from files containing those lines; and ROSF [51], which recommends code snippets against a free-form query by first generating a candidate set of snippets using information retrieval followed by re-ranking the code snippets using a learned prediction model that is trained on a set of user queries and code-snippet features such as text, topic, and structure.…”

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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Section: Code Recommendation Systemsmentioning

confidence: 99%