A Simple, Efficient, Context‐sensitive Approach for Code Completion

Asaduzzaman, Muhammad; Roy, Chanchal K.; Schneider, Kevin A.; Hou, Daqing

doi:10.1002/smr.1791

Cited by 17 publications

(10 citation statements)

References 34 publications

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“…Completions may simply be method calls for a given object [74,[76][77][78][79] or can be complete method code for a given partial code snippet [75]. Code completion is an integral feature of modern IDEs [79]. Most of the proposed techniques enable the integration of the code completion recommendations into the active user context, typically within their Integrated Development Environment (IDE).…”

Section: Code Completion 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 Completion 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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“…The initial values of parameters α, β, γ, δ, and θ are set as 0.0, 0.0, 1.0, 1.0, and 1.0. In each iteration, we go through the five parameters and check the 101 possible values (Lines [6][7][8][9][10][11][12][13][14]. The function eval() is used to check the effectiveness of a specific parameter with respect to the criteria from online evaluation.…”

Section: Algorithm 1 Greedy Weight Tuning Algorithmmentioning

confidence: 99%

“…The online evaluation is often infeasible due to the high cost of human labor [10]. To overcome these limitations, many researchers turn to offline evaluation [11][12][13][14][15][16]. However, offline evaluations cannot always reflect the accurate quality of the system due to the lack of mature evaluation model [17].…”

Section: Introductionmentioning

confidence: 99%

An analysis of correctness for API recommendation: are the unmatched results useless?

Kong

Weina

Liao

et al. 2020

Sci. China Inf. Sci.

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API recommendation is a promising approach which is widely used during software development. However, the evaluation of API recommendation is not explored with sufficient rigor. The current evaluation of API recommendation mainly focuses on correctness, the measurement is conducted by matching recommended results with ground-truth results. In most cases, there is only one set of ground-truth APIs for each recommendation attempt, but the object code can be implemented in dozens of ways. The neglect of code diversity results in a possible defect in the evaluation. To address the problem, we invite 15 developers to analyze the unmatched results in a user study. The online evaluation confirms that some unmatched APIs can also benefit to programming due to the functional correlation with ground-truth APIs. Then we measure the API functional correlation based on the relationships extracted from API knowledge graph, API method name, and API documentation. Furthermore, we propose an approach to improve the measurement of correctness based on API functional correlation. Our measurement is evaluated on a dataset of 6141 requirements and historical code fragments from related commits. The results show that 28.2% of unmatched APIs can contribute to correctness in our experiments.

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“…Unlike our parameter concretization technique, their technique does not consider the relation between the current method call and the code context. Some approaches [26,27] consider the similarity between the current code context and previous code examples for code completion. Other example approaches include [4,6,8,[28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Generative API usage code recommendation with parameter concretization

Chen

Peng

Sun

et al. 2019

Sci. China Inf. Sci.

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Many programming languages and development frameworks have extensive libraries (e.g., JDK and Android libraries) that ease the task of software engineering if used effectively. With numerous library classes and sometimes intricate API (application programming interface) usage constraints, programmers often have difficulty remembering the library APIs and/or using them correctly. This study addresses this problem by developing an engine called DeepAPIRec, which automatically recommends the API usage code. Compared to the existing proposals, our approach distinguishes itself in two ways. First, it is based on a tree-based long short-term memory (LSTM) neural network inspired by recent developments in the machinelearning community. A tree-based LSTM neural network allows us to model and reason about variable-length, preceding and succeeding code contexts, and to make precise predictions. Second, we apply data-flow analysis to generate concrete parameters for the API usage code, which not only allows us to generate complete code recommendations but also improves the accuracy of the learning results according to the tree-based LSTM neural network. Our approach has been implemented for supporting Java programs. Our experimental studies on the JDK library show that at statement-level recommendations, DeepAPIRec can achieve a top-1 accuracy of about 37% and a top-5 accuracy of about 64%, which are significantly better than the existing approaches. Our user study further confirms that DeepAPIRec can help developers to complete a segment of code faster and more accurately as compared to IntelliJ IDEA.

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A Simple, Efficient, Context‐sensitive Approach for Code Completion

Cited by 17 publications

References 34 publications

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

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

An analysis of correctness for API recommendation: are the unmatched results useless?

Generative API usage code recommendation with parameter concretization

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