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30th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Khan, Junaed Younus Uddin, Gias Univ Calgary DISA Lab Calgary AB Canada

ISBN: (纸本)9781665452786

code example is a crucial part of good documentation. It helps the developers to understand the documentation easily and use the corresponding code unit (e.g., method) properly. However, many official documentation still lacks (good) code example and it is one of the common documentation issues as found by several studies. Hence in this paper, we consider automatic code example generation for documentation, a direction less explored by the existing research. We employ codex, a GPT3 based model, pre-trained on both natural and programming languages to generate code examples from source code and documentation given as input. Our preliminary investigation on 40 scikit-learn methods reveals that this approach is able to generate good code examples where 72.5% code examples were executed without error (passability) and 82.5% properly dealt with the target method and documentation (relevance). We also find that incorporation of error logs (produced by the compiler while executing a failed code example) in the input further improves the passability from 72.5% to 87.5%. Thus, our investigation sets the base of documentation-specific code example generation and warrants in-depth future studies.

关键词： code example code Documentation Machine Learning GPT-3 codex

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What Makes a Good code example? A Study of Programming Q&A in StackOverflow

What Makes a Good Code Example? A Study of Programming Q&A i...

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28th International Conference on Software Maintenance (ICSM)

作者： Nasehi, Seyed Mehdi Sillito, Jonathan Maurer, Frank Burns, Chris Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

ISBN: (纸本)9781467323123

Programmers learning how to use an API or a programming language often rely on code examples to support their learning activities. However, what makes for an effective code example remains an open question. Finding the characteristics of the effective examples is essential in improving the appropriateness of these learning aids. To help answer this question we have conducted a qualitative analysis of the questions and answers posted to a programming Q&A web site called StackOverflow. On StackOverflow answers can be voted on, indicating which answers were found helpful by users of the site. By analyzing these well-received answers we identified characteristics of effective examples. We found that the explanations accompanying examples are as important as the examples themselves. Our findings have implications for the way the API documentation and example set should be developed and evolved as well as the design of the tools assisting the development of these materials.

关键词： code example documentation API social learning

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Learning to rank code examples for code search engines

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EMPIRICAL SOFTWARE ENGINEERING 2017年第1期22卷 259-291页

作者： Niu, Haoran Keivanloo, Iman Zou, Ying Queens Univ Dept Elect & Comp Engn Kingston ON Canada

Source code examples are used by developers to implement unfamiliar tasks by learning from existing solutions. To better support developers in finding existing solutions, code search engines are designed to locate and rank code examples relevant to user's queries. Essentially, a code search engine provides a ranking schema, which combines a set of ranking features to calculate the relevance between a query and candidate code examples. Consequently, the ranking schema places relevant code examples at the top of the result list. However, it is difficult to determine the configurations of the ranking schemas subjectively. In this paper, we propose a code example search approach that applies a machine learning technique to automatically train a ranking schema. We use the trained ranking schema to rank candidate code examples for new queries at run-time. We evaluate the ranking performance of our approach using a corpus of over 360,000 code snippets crawled from 586 open-source Android projects. The performance evaluation study shows that the learning-to-rank approach can effectively rank code examples, and outperform the existing ranking schemas by about 35.65 % and 48.42 % in terms of normalized discounted cumulative gain (NDCG) and expected reciprocal rank (ERR) measures respectively.

关键词： Learning to rank code example code search code recommendation

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Recommending Proper API code examples for Documentation Purpose

Recommending Proper API Code Examples for Documentation Purp...

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18th Asia-Pacific Software Engineering Conference (APSEC)

作者： Mar, Lee Wei Wu, Ye-Chi Jiau, Hewijin Christine Natl Cheng Kung Univ Inst Comp & Commun Engn Tainan Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan

ISBN: (纸本)9780769546094

code examples are important resources for expressing correct application programming interface (API) usages. However, many framework and library APIs fail in offering sufficient code examples in corresponding API documentations. This is because constructing proper code examples for documentation purpose takes significant developers' efforts. To reduce such effort, this work proposes a methodology, PropER- Doc, that recommends proper code examples for documentation purpose. PropER- Doc accepts queries from API developers and utilizes code search engines (CSEs) to collect corresponding code example candidates. The structural and conceptual links between API elements are captured from the API implementation and available API documents to guide candidate recommendation. During recommendation, PropER- Doc groups collected candidates based on involved API types for distinguishing different API usages. To assist API developers in selecting proper candidates, a diagrammatic presentation and three code example appropriateness metrics are also developed in PropER- Doc. Two case studies on Eclipse JDT framework are conducted to confirm the effectiveness of PropER- Doc.

关键词： framework documentation API usage code example

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CSCC: Simple, Efficient, Context Sensitive code Completion 30

CSCC: Simple, Efficient, Context Sensitive Code Completion

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30th IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Asaduzzaman, Muhammad Roy, Chanchal K. Schneider, Kevin A. Hou, Daqing Univ Saskatchewan Dept Comp Sci Saskatoon SK S7N 0W0 Canada Clarkson Univ Elect & Comp Engn Dept Potsdam NY 13676 USA

ISBN: (纸本)9780769553030

code Completion helps developers learn APIs and frees them from remembering every detail. In this paper, we describe a novel technique called CSCC (Context Sensitive code Completion) 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 contexts. 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 also investigate how the different contextual elements of the target call benefit CSCC.

关键词： code completion code example recommendation simhash

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Methods for Categorizing and Recommending API Usage Patterns Based on Degree Centralities and Pattern Distances

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2018年第3期34卷 593-610页

作者： Lee, Shin-Jie Su, Wu-Chen Huang, Chi-En You, Jie-Lin Natl Cheng Kung Univ Comp & Network Ctr Tainan 701 Taiwan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan Univ Kentucky Dept Clin Sci Lexington KY 40506 USA

API usage patterns have been considered as significant materials in reusing software library APIs for saving development time and improving software quality. Although efforts have been made on discovering and searching API usage patterns, the following two issues are still largely unexplored: how to provide a well-organised view of the discovered API usage patterns? and how to recommend follow-up API usage patterns once a usage pattern is adopted? This paper proposes two methods for categorizing and recommending API usage patterns: first, categories of the usage patterns are automatically identified based on a proposed degree centrality-based clustering algorithm;and second, follow-up usage patterns of an adopted pattern are recommended based on a proposed metric of measuring distances between patterns. In the experimental evaluations, the patterns categorization can achieve 85.4% precision rate with 83% recall rate. The patterns recommendation had approximately half a chance of correctly predicting the follow-up patterns that were actually used by the programmers.

关键词： API usage pattern code example code search system API usage patterns categorization API usage patterns recommendation

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Unit Tests as API Usage examples

Unit Tests as API Usage Examples

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International Conference on Software Maintenance

作者： Nasehi, Seyed Mehdi Maurer, Frank Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

ISBN: (纸本)9781424486298

This study aims to find out if API unit tests can provide good usage examples, and if so, what prevents developers from finding and using those examples. The results of an experiment we performed with two groups of developers showed that unit tests can be very helpful, especially when the task is complicated and involves multiple classes and methods. Well-written tests proved to be a good source of examples, but finding the relevant examples using the standard tools might be very difficult. We propose to supplement the standard API documentation with relevant examples taken from the unit tests. To further improve the learnability of the API, presentation of the documentation and examples has to be tailored in a way that separates or hides advanced usage scenarios from the commonly used ones.

关键词： API Usability Unit Test Documentation code example

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Categorizing and Recommending API Usage Patterns based on Degree Centralities and Pattern Distances

Categorizing and Recommending API Usage Patterns based on De...

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International Computer Symposium (ICS)

作者： Lee, Shin-Jie Su, Wu-Chen Huang, Chi-En You, Jie-Lin Natl Cheng Kung Univ Comp & Network Ctr Tainan Taiwan Univ Kentucky Dept Clin Sci Lexington KY USA Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781509034383

Although efforts have been made on discovering and searching API usage patterns, how to categorize and recommend follow-up API usage patterns is still largely unexplored. This paper advances the state-of-the-art by proposing two methods for categorizing and recommending API usage patterns: first, categories of the usage patterns are automatically identified based on a proposed degree centrality-based clustering algorithm;and second, follow-up usage patterns of an adopted pattern are recommended based on a proposed metric of measuring distances between patterns. In the experimental evaluations, the patterns categorization can achieve 85.4% precision rate with 83% recall rate. The patterns recommendation had approximately half a chance of correctly predicting the follow-up patterns that were actually used by the programmers.

关键词： API usage pattern code example code search system API usage patterns categorization API usage patterns recommendation

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Recommending Framework Extension examples

Recommending Framework Extension Examples

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33rd IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Asaduzzaman, Muhammad Roy, Chanchal K. Schneider, Kevin A. Hou, Daqing Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada Clarkson Univ Elect & Comp Engn Dept New York NY USA

ISBN: (纸本)9781538609927

The use of software frameworks enables the delivery of common functionality but with significantly less effort than when developing from scratch. To meet application specific requirements, the behavior of a framework needs to be customized via extension points. A common way of customizing framework behavior is by passing a framework related object as an argument to an API call. Such an object can be created by subclassing an existing framework class or interface, or by directly customizing an existing framework object. However, to do this effectively requires developers to have extensive knowledge of the framework's extension points and their interactions. To aid the developers in this regard, we propose and evaluate a graph mining approach for extension point management. Specifically, we propose a taxonomy of extension patterns to categorize the various ways an extension point has been used in the code examples. Our approach mines a large amount of code examples to discover all extension points and patterns for each framework class. Given a framework class that is being used, our approach aids the developer by following a two-step recommendation process. First, it recommends all the extension points that are available in the class. Once the developer chooses an extension point, our approach then discovers all of its usage patterns and recommends the best code examples for each pattern. Using five frameworks, we evaluate the performance of our two-step recommendation, in terms of precision, recall, and F-measure. We also report several statistics related to framework extension points.

关键词： reuse API framework extension point extension pattern code example graph mining recommender

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Servo 2011 01

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2016年

Servo 2011 01 by published by

关键词： arduino arduino ide code example combots cup digital pin motor motor assembly pinguino program programming robot robot hands robotics robots servo sewer snake simulated robot smartsensor lite usb void setup

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