Cookbook: in situ code completion using edit recipes learned from examples

Jacobellis, John; Meng, Na; Kim, Miryung

doi:10.1145/2591062.2591076

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Code completion. Cookbook [19] uses generalizations from examples to suggest code completions while the developer is typing. In contrast to ARES it is a line based approach and does not support code movements and thus has a lower accuracy if code movements are necessary.…”

Section: Related Workmentioning

confidence: 99%

More accurate recommendations for method-level changes

Dotzler

Kamp

Kreutzer

et al. 2017

Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

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During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations. Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations. In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples. Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments.We present the Accurate REcommendation System (ARES) that achieves a higher accuracy than other tools because its algorithms take care of code movements when creating patterns and recommendations. On average, the recommendations by ARES have an accuracy of 96% with respect to code changes that developers have manually performed in commits of source code archives. At the same time ARES achieves precision and recall values that are on par with other tools.

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Section: Related Workmentioning

confidence: 99%

More accurate recommendations for method-level changes

Dotzler

Kamp

Kreutzer

et al. 2017

Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

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“…It also helps developers to navigate to the past code examples to see the changes. Jacobellis et al leverage code completion to automate the edit operations of source code from user specified custom, reusable template of code changes.…”

Section: Related Workmentioning

confidence: 99%

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

Asaduzzaman

Roy

Schneider

et al. 2016

J Software Evolu Process

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Code completion helps developers use application programming interfaces (APIs) and frees them from remembering every detail. In this paper, we first describe a novel technique called Context‐sensitive Code Completion (CSCC) for improving the performance of API method call completion. CSCC is context sensitive in that it uses new sources of information as the context of a target method call. CSCC indexes method calls in code examples by their context. To recommend completion proposals, CSCC ranks candidate methods by the similarities between their contexts and the context of the target call. Evaluation using a set of subject systems and five popular state‐of‐the‐art techniques suggests that CSCC performs better than existing type or example‐based code completion systems. We conduct experiments to find how different contextual elements of the target call benefit CSCC. Next, we investigate the adaptability of the technique to support another form of code completion, i.e., field completion. Evaluation with eight different subject systems suggests that CSCC can easily support field completion with high accuracy. Finally, we compare CSCC with four popular statistical language models that support code completion. Results of statistical tests from our study suggest that CSCC not only outperforms those techniques that are based on token level language models, but also in most cases performs better or equally well with GraLan, the state‐of‐the‐art graph‐based language model. Copyright © 2016 John Wiley & Sons, Ltd.

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“…For such a spot, LASE recommends a code change that is adapted to the spot's context. Cookbook [30] uses the generalizations to suggest code completions while the developer is typing. RASE [41] also starts from a set of similar changes.…”

Section: Introductionmentioning

confidence: 99%

Automatic clustering of code changes

Kreutzer

Dotzler

Ring³

et al. 2016

Proceedings of the 13th International Conference on Mining Software Repositories

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Several research tools and projects require groups of similar code changes as input. Examples are recommendation and bug finding tools that can provide valuable information to developers based on such data. With the help of similar code changes they can simplify the application of bug fixes and code changes to multiple locations in a project. But despite their benefit, the practical value of existing tools is limited, as users need to manually specify the input data, i.e., the groups of similar code changes.To overcome this drawback, this paper presents and evaluates two syntactical similarity metrics, one of them is specifically designed to run fast, in combination with two carefully selected and self-tuning clustering algorithms to automatically detect groups of similar code changes.We evaluate the combinations of metrics and clustering algorithms by applying them to several open source projects and also publish the detected groups of similar code changes online as a reference dataset. The automatically detected groups of similar code changes work well when used as input for LASE, a recommendation system for code changes.

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Cookbook: in situ code completion using edit recipes learned from examples

Cited by 5 publications

References 9 publications

More accurate recommendations for method-level changes

More accurate recommendations for method-level changes

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

Automatic clustering of code changes

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