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Enriching api documentation with Code Samples and Usage Scenarios from Crowd Knowledge

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2021年第6期47卷 1299-1314页

作者： Zhang, Jingxuan Jiang, He Ren, Zhilei Zhang, Tao Huang, Zhiqiu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Dalian Univ Technol Dalian 116081 Liaoning Peoples R China Beijing Inst Technol Beijing 100091 Peoples R China Dalian Univ Technol Sch Software Dalian 116081 Liaoning Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150006 Heilongjiang Peoples R China

As one key resource to learn Application Programming Interfaces (apis), a lot of api reference documentation lacks code samples with usage scenarios, thus heavily hindering developers from programming with apis. Although researchers have investigated how to enrich api documentation with code samples from general code search engines, two main challenges remain to be resolved, including the quality challenge of acquiring high-quality code samples and the mapping challenge of matching code samples to usage scenarios. In this study, we propose a novel approach named ADECK towards enriching api documentation with code samples and corresponding usage scenarios by leveraging crowd knowledge from Stack Overflow, a popular technical Question and Answer (Q&A) website attracting millions of developers. Given an api related Q&A pair, a code sample in the answer is extensively evaluated by developers and targeted towards resolving the question under the specified usage scenario. Hence, ADECK can obtain high-quality code samples and map them to corresponding usage scenarios to address the above challenges. Extensive experiments on the Java SE and Android api documentation show that the number of code-sample-illustrated api types in the ADECK-enriched api documentation is 3.35 and 5.76 times as many as that in the raw api documentation. Meanwhile, the quality of code samples obtained by ADECK is better than that of code samples by the baseline approach eXoaDocs in terms of correctness, conciseness, and usability, e.g., the average correctness values of representative code samples obtained by ADECK and eXoaDocs are 4.26 and 3.28 on a 5-point scale in the enriched Java SE api documentation. In addition, an empirical study investigating the impacts of different types of api documentation on the productivity of developers shows that, compared against the raw and the eXoaDocs-enriched api documentation, the ADECK-enriched api documentation can help developers complete 23.81 and 14.29 p

关键词： documentation Programming Java Knowledge engineering Search engines Productivity Task analysis api documentation code sample usage scenario stack overflow crowd knowledge

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Recommending reference api documentation

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EMPIRICAL SOFTWARE ENGINEERING 2015年第6期20卷 1558-1586页

作者： Robillard, Martin P. Chhetri, Yam B. McGill Univ Sch Comp Sci Montreal PQ Canada

Reference documentation is an important source of information on api usage. However, information useful to programmers can be buried in irrelevant text, or attached to a non-intuitive api element, making it difficult to discover. We propose to detect and recommend fragments of api documentation potentially important to a programmer who has already decided to use a certain api element. We categorize text fragments in api documentation based on whether they contain information that is indispensable, valuable, or neither. From the fragments that contain knowledge worthy of recommendation, we extract word patterns, and use these patterns to automatically find new fragments that contain similar knowledge in unseen documentation. We implemented our technique in a tool, Krec, that supports both information filtering and discovery. In an evaluation study with randomly-sampled method definitions from ten open source systems, we found that with a training set derived from about 1000 documentation units, we could issue recommendations with 90 % precision and 69 % recall. In a study involving ten independent assessors, indispensable knowledge items recommended for api types were judged useful 57 % of the time and potentially useful an additional 30 % of the time.

关键词： Application programming interfaces api documentation Text classification Natural language processing Recommendation systems

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Automatic Detection and Repair Recommendation of Directive Defects in Java api documentation

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2020年第9期46卷 1004-1023页

作者： Zhou, Yu Wang, Changzhi Yan, Xin Chen, Taolue Panichella, Sebastiano Gall, Harald Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Peoples R China Birkbeck Univ London Dept Comp Sci & Informat Syst London WC1E 7HX England Southwest Univ RISE Chongqing 400716 Peoples R China Zurich Univ Appl Sci Dept Informat CH-8401 Winterthur Switzerland Univ Zurich Dept Informat CH-8006 Zurich Switzerland

Application Programming Interfaces (apis) represent key tools for software developers to build complex software systems. However, several studies have revealed that even major api providers tend to have incomplete or inconsistent api documentation. This can severely hamper the api comprehension and, as a consequence, the quality of the software built on them. In this paper, we propose DRONE (Detect and Repair of documentation dEfects), a framework to automatically detect and repair defects from api documents by leveraging techniques from program analysis, natural language processing, and constraint solving. Specifically, we target at the directives of api documents, which are related to parameter constraints and exception handling declarations. Furthermore, in presence of defects, we also provide a prototypical repair recommendation system. We evaluate our approach on parts of the well-documented apis of JDK 1.8 apis (including javaFX) and Android 7.0 (level 24). Across the two empirical studies, our approach can detect api defects with an average F-measure of 79.9, 71.7, and 81.4 percent, respectively. The api repairing capability has also been evaluated on the generated recommendations in a further experiment. User judgments indicate that the constraint information is addressed correctly and concisely in the rendered directives.

关键词： documentation Maintenance engineering Software Drones Androids Humanoid robots Facebook api documentation directive defects natural language processing repair recommendation

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"I just looked for the solution!"On Integrating Security-Relevant Information in Non-Security api documentation to Support Secure Coding Practices

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第9期48卷 3467-3484页

作者： Gorski, Peter Leo Moeller, Sebastian Wiefling, Stephan Lo Iacono, Luigi H BRS Univ Appl Sci Dept Comp Sci Data & Applicat Secur Grp D-53757 St Augustin Germany TU Berlin Qual & Usabil Lab D-10623 Berlin Germany

Software developers build complex systems using plenty of third-party libraries. documentation is key to understand and use the functionality provided via the libraries' apis. Therefore, functionality is the main focus of contemporary api documentation, while cross-cutting concerns such as security are almost never considered at all, especially when the api itself does not provide security features. documentations of JavaScript libraries for use in web applications, e.g., do not specify how to add or adapt a Content Security Policy (CSP) to mitigate content injection attacks like Cross-Site Scripting (XSS). This is unfortunate, as security-relevant api documentation might have an influence on secure coding practices and prevailing major vulnerabilities such as XSS. For the first time, we study the effects of integrating security-relevant information in non-security api documentation. For this purpose, we took CSP as an exemplary study object and extended the official Google Maps JavaScript api documentation with security-relevant CSP information in three distinct manners. Then, we evaluated the usage of these variations in a between-group eye-tracking lab study involving N=49 participants. Our observations suggest: (1) Developers are focused on elements with code examples. They mostly skim the documentation while searching for a quick solution to their programming task. This finding gives further evidence to results of related studies. (2) The location where CSP-related code examples are placed in non-security api documentation significantly impacts the time it takes to find this security-relevant information. In particular, the study results showed that the proximity to functional-related code examples in documentation is a decisive factor. (3) Examples significantly help to produce secure CSP solutions. (4) Developers have additional information needs that our approach cannot meet. Overall, our study contributes to a first understanding of the impact of security-r

关键词： documentation Security Software Task analysis Internet Encoding Usability api documentation content security policies secure coding practices developer centered security usable security

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Inferring specifications for resources from natural language api documentation

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AUTOMATED SOFTWARE ENGINEERING 2011年第3-4期18卷 227-261页

作者： Zhong, Hao Zhang, Lu Xie, Tao Mei, Hong Chinese Acad Sci Inst Software Lab Internet Software Technol Beijing Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Peking Univ Minist Educ Key Lab High Confidence Software Technol Beijing 100871 Peoples R China N Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA

Many software libraries, especially those commercial ones, provide api documentation in natural languages to describe correct api usages. However, developers may still write code that is inconsistent with api documentation, partially because many developers are reluctant to carefully read api documentation as shown by existing research. As these inconsistencies may indicate defects, researchers have proposed various detection approaches, and these approaches need many known specifications. As it is tedious to write specifications manually for all apis, various approaches have been proposed to mine specifications automatically. In the literature, most existing mining approaches rely on analyzing client code, so these mining approaches would fail to mine specifications when client code is not sufficient. Instead of analyzing client code, we propose an approach, called Doc2Spec, that infers resource specifications from api documentation in natural languages. We evaluated our approach on the Javadocs of five libraries. The results show that our approach performs well on real scale libraries, and infers various specifications with relatively high precisions, recalls, and F-scores. We further used inferred specifications to detect defects in open source projects. The results show that specifications inferred by Doc2Spec are useful to detect real defects in existing projects.

关键词： Inferring specifications api documentation

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Optimizing api documentation Some Guidelines and Effects 20

Optimizing API Documentation Some Guidelines and Effects

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38th ACM International Conference on the Design of Communication (SIGDOC)

作者： Meng, Michael Steinhardt, Stephanie M. Schubert, Andreas Merseburg Univ Appl Sci Dept Business Studies & Informat Sci Merseburg Germany

ISBN: (纸本)9781450375252

The growing importance of apis creates a need to support developers with effective documentation. Prior research has generated important findings regarding information needs of developers and expectations they form towards api documentation. Several guidelines have been proposed on the basis of these findings, but evidence is lacking whether such guidelines actually lead to better documentation. This paper contributes the results of an empirical test that compared the performance of two groups of developers working on a set of pre-defined tasks with an api they were unfamiliar with. One group had access to documentation which was optimized following guidelines for api documentation design proposed in the literature whereas the other group used non-optimized documentation. Results show that developers working with optimized documentation made fewer errors on the test tasks and were faster in planning and executing the tasks. We conclude that the guidelines used in our study do have the intended effect and effectively support initial interactions with an api.

关键词： api documentation user studies information design usability

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CLCDSA: Cross Language Code Clone Detection using Syntactical Features and api documentation 34

CLCDSA: Cross Language Code Clone Detection using Syntactica...

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34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Nafi, Kawser Wazed Kar, Tonny Shekha Roy, Banani Roy, Chanchal K. Schneider, Kevin A. Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (纸本)9781728125084

Software clones are detrimental to software maintenance and evolution and as a result many clone detectors have been proposed. These tools target clone detection in software applications written in a single programming language. However, a software application may be written in different languages for different platforms to improve the application's platform compatibility and adoption by users of different platforms. Cross language clones (CLCs) introduce additional challenges when maintaining multi-platform applications and would likely go undetected using existing tools. In this paper, we propose CLCDSA, a cross language clone detector which can detect CLCs without extensive processing of the source code and without the need to generate an intermediate representation. The proposed CLCDSA model analyzes different syntactic features of source code across different programming languages to detect CLCs. To support large scale clone detection, the CLCDSA model uses an action filter based on cross language api call similarity to discard non-potential clones. The design methodology of CLCDSA is two-fold: (a) it detects CLCs on the fly by comparing the similarity of features, and (b) it uses a deep neural network based feature vector learning model to learn the features and detect CLCs. Early evaluation of the model observed an average precision, recall and F-measure score of 0.55, 0.86, and 0.64 respectively for the first phase and 0.61, 0.93, and 0.71 respectively for the second phase which indicates that CLCDSA outperforms all available models in detecting cross language clones.

关键词： Code Clone api documentation Word2Vector Source Code Syntax

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apiBot: Question Answering Bot for api documentation 32

APIBot: Question Answering Bot for API Documentation

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32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Tian, Yuan Thung, Ferdian Sharma, Abhishek Lo, David Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781538626849

As the carrier of Application Programming Interfaces (apis) knowledge, api documentation plays a crucial role in how developers learn and use an api. It is also a valuable information resource for answering api-related questions, especially when developers cannot find reliable answers to their questions online/offline. However, finding answers to api-related questions from api documentation might not be easy because one may have to manually go through multiple pages before reaching the relevant page, and then read and understand the information inside the relevant page to figure out the answers. To deal with this challenge, we develop apiBot, a bot that can answer api questions given api documentation as an input. apiBot is built on top of SiriusQA, the QA system from Sirius, a state of the art intelligent personal assistant. To make SiriusQA work well under software engineering scenario, we make several modifications over SiriusQA by injecting domain specific knowledge. We evaluate apiBot on 92 api questions, answers of which are known to be present in Java 8 documentation. Our experiment shows that apiBot can achieve a Hit@5 score of 0.706.

关键词： api documentation Question Answering Bot

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Understanding Performance Concerns in the api documentation of Data Science Libraries 35

Understanding Performance Concerns in the API Documentation ...

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35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Tao, Yida Jiang, Jiefang Liu, Yepang Xu, Zhiwu Qin, Shengchao Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England

ISBN: (纸本)9781450367684

The development of efficient data science applications is often impeded by unbearably long execution time and rapid RAM exhaustion. Since api documentation is the primary information source for troubleshooting, we investigate how performance concerns are documented in popular data science libraries. Our quantitative results reveal the prevalence of data science apis that are documented in performance-related context and the infrequent maintenance activities on such documentation. Our qualitative analyses further reveal that crowd documentation like Stack Overflow and GitHub are highly complementary to official documentation in terms of the api coverage, the knowledge distribution, as well as the specific information conveyed through performance-related content. Data science practitioners could benefit from our findings by learning a more targeted search strategy for resolving performance issues. Researchers can be more assured of the advantages of integrating both the official and the crowd documentation to achieve a holistic view on the performance concerns in data science development.

关键词： api documentation performance data science empirical study

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Augmenting api documentation with Insights from Stack Overflow 16

Augmenting API Documentation with Insights from Stack Overfl...

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38th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Treude, Christoph Robillard, Martin P. Univ Adelaide Sch Comp Sci Adelaide SA Australia McGill Univ Sch Comp Sci Montreal PQ Canada

ISBN: (纸本)9781450339001

Software developers need access to different kinds of information which is often dispersed among different documentation sources, such as api documentation or Stack Over flow. We present an approach to automatically augment api documentation with\ insight sentences" from Stack Overflow| sentences that are related to a particular api type and that provide insight not contained in the api documentation of that type. Based on a development set of 1,574 sentences, we compare the performance of two state-of-the-art summarization techniques as well as a pattern-based approach for insight sentence extraction. We then present SISE, a novel machine learning based approach that uses as features the sentences themselves, their formatting, their question, their answer, and their authors as well as part-of-speech tags and the similarity of a sentence to the corresponding api documentation. With SISE, we were able to achieve a precision of 0.64 and a coverage of 0.7 on the development set. In a comparative study with eight software developers, we found that SISE resulted in the highest number of sentences that were considered to add useful information not found in the api documentation. These results indicate that taking into account the meta data available on Stack Over flow as well aspart- of-speech tags can significantly improve unsupervised extraction approaches when applied to Stack Overflow data.

关键词： api documentation Stack Overflow insight sentences

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