Hitesh Sajnani, V. Saini, Jeffrey Svajlenko, C. Roy, C. Lopes
Despite a decade of active research, there has been a marked lack in clone detection techniques that scale to large repositories for detecting near-miss clones. In this paper, we present a token-based clone detector, SourcererCC, that can detect both exact and near-miss clones from large inter-project repositories using a standard workstation. It exploits an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone. We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks: (1) a big benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (25K projects, 250MLOC) using a standard workstation.
{"title":"SourcererCC: Scaling Code Clone Detection to Big-Code","authors":"Hitesh Sajnani, V. Saini, Jeffrey Svajlenko, C. Roy, C. Lopes","doi":"10.1145/2884781.2884877","DOIUrl":"https://doi.org/10.1145/2884781.2884877","url":null,"abstract":"Despite a decade of active research, there has been a marked lack in clone detection techniques that scale to large repositories for detecting near-miss clones. In this paper, we present a token-based clone detector, SourcererCC, that can detect both exact and near-miss clones from large inter-project repositories using a standard workstation. It exploits an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone. We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks: (1) a big benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (25K projects, 250MLOC) using a standard workstation.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"36 1","pages":"1157-1168"},"PeriodicalIF":0.0,"publicationDate":"2015-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88264034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern programming frameworks come with large libraries, with diverse applications such as for matching regular expressions, parsing XML files and sending email. Programmers often use search engines such as Google and Bing to learn about existing APIs. In this paper, we describe SWIM, a tool which suggests code snippets given API-related natural language queries such as "generate md5 hash code". We translate user queries into the APIs of interest using clickthrough data from the Bing search engine. Then, based on patterns learned from open-source code repositories, we synthesize idiomatic code describing the use of these APIs. We introduce emph{structured call sequences} to capture API-usage patterns. Structured call sequences are a generalized form of method call sequences, with if-branches and while-loops to represent conditional and repeated API usage patterns, and are simple to extract and amenable to synthesis. We evaluated SWIM with 30 common C# API-related queries received by Bing. For 70% of the queries, the first suggested snippet was a relevant solution, and a relevant solution was present in the top 10 results for all benchmarked queries. The online portion of the workflow is also very responsive, at an average of 1.5 seconds per snippet.
现代编程框架都带有大型库，以及各种各样的应用程序，比如匹配正则表达式、解析XML文件和发送电子邮件。程序员经常使用谷歌和必应等搜索引擎来了解现有的api。在本文中，我们描述了SWIM，这是一个工具，它给出了与api相关的自然语言查询(如“生成md5哈希码”)的代码片段。我们使用必应搜索引擎的点击数据将用户查询转换为感兴趣的api。然后，基于从开源代码存储库学习到的模式，我们合成描述这些api使用的惯用代码。我们引入emph{结构化调用序列}来捕获api使用模式。结构化调用序列是方法调用序列的一种通用形式，使用if分支和while循环来表示有条件的和重复的API使用模式，并且易于提取并且易于合成。我们用Bing收到的30个常见的c＃ api相关查询对SWIM进行了评估。70岁% of the queries, the first suggested snippet was a relevant solution, and a relevant solution was present in the top 10 results for all benchmarked queries. The online portion of the workflow is also very responsive, at an average of 1.5 seconds per snippet.
{"title":"SWIM: Synthesizing What I Mean - Code Search and Idiomatic Snippet Synthesis","authors":"Mukund Raghothaman, Yi Wei, Y. Hamadi","doi":"10.1145/2884781.2884808","DOIUrl":"https://doi.org/10.1145/2884781.2884808","url":null,"abstract":"Modern programming frameworks come with large libraries, with diverse applications such as for matching regular expressions, parsing XML files and sending email. Programmers often use search engines such as Google and Bing to learn about existing APIs. In this paper, we describe SWIM, a tool which suggests code snippets given API-related natural language queries such as \"generate md5 hash code\". We translate user queries into the APIs of interest using clickthrough data from the Bing search engine. Then, based on patterns learned from open-source code repositories, we synthesize idiomatic code describing the use of these APIs. We introduce emph{structured call sequences} to capture API-usage patterns. Structured call sequences are a generalized form of method call sequences, with if-branches and while-loops to represent conditional and repeated API usage patterns, and are simple to extract and amenable to synthesis. We evaluated SWIM with 30 common C# API-related queries received by Bing. For 70% of the queries, the first suggested snippet was a relevant solution, and a relevant solution was present in the top 10 results for all benchmarked queries. The online portion of the workflow is also very responsive, at an average of 1.5 seconds per snippet.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"23 1","pages":"357-367"},"PeriodicalIF":0.0,"publicationDate":"2015-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88091775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu-Fang Chen, Chiao-En Hsieh, Ondřej Lengál, Tsung-Ju Lii, M. Tsai, Bow-Yaw Wang, Farn Wang
We introduce a novel technique for verification and model synthesis of sequential programs. Our technique is based on learning an approximate regular model of the set of feasible paths in a program, and testing whether this model contains an incorrect behavior. Exact learning algorithms require checking equivalence between the model and the program, which is a difficult problem, in general undecidable. Our learning procedure is therefore based on the framework of probably approximately correct (PAC) learning, which uses sampling instead, and provides correctness guarantees expressed using the terms error probability and confidence. Besides the verification result, our procedure also outputs the model with the said correctness guarantees. Obtained preliminary experiments show encouraging results, in some cases even outperforming mature software verifiers.
{"title":"PAC Learning-Based Verification and Model Synthesis","authors":"Yu-Fang Chen, Chiao-En Hsieh, Ondřej Lengál, Tsung-Ju Lii, M. Tsai, Bow-Yaw Wang, Farn Wang","doi":"10.1145/2884781.2884860","DOIUrl":"https://doi.org/10.1145/2884781.2884860","url":null,"abstract":"We introduce a novel technique for verification and model synthesis of sequential programs. Our technique is based on learning an approximate regular model of the set of feasible paths in a program, and testing whether this model contains an incorrect behavior. Exact learning algorithms require checking equivalence between the model and the program, which is a difficult problem, in general undecidable. Our learning procedure is therefore based on the framework of probably approximately correct (PAC) learning, which uses sampling instead, and provides correctness guarantees expressed using the terms error probability and confidence. Besides the verification result, our procedure also outputs the model with the said correctness guarantees. Obtained preliminary experiments show encouraging results, in some cases even outperforming mature software verifiers.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"71 1","pages":"714-724"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78885573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aditya Desai, Sumit Gulwani, V. Hingorani, Nidhi Jain, Amey Karkare, Mark Marron, R. Sailesh, Subhajit Roy
Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.
{"title":"Program Synthesis Using Natural Language","authors":"Aditya Desai, Sumit Gulwani, V. Hingorani, Nidhi Jain, Amey Karkare, Mark Marron, R. Sailesh, Subhajit Roy","doi":"10.1145/2884781.2884786","DOIUrl":"https://doi.org/10.1145/2884781.2884786","url":null,"abstract":"Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"63 1","pages":"345-356"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86878195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scalability is a major challenge for existing behavioral log analysis algorithms, which extract finite-state automaton models or temporal properties from logs generated by running systems. In this paper we present statistical log analysis, which addresses scalability using statistical tools. The key to our approach is to consider behavioral log analysis as a statistical experiment.Rather than analyzing the entire log, we suggest to analyze only a sample of traces from the log and, most importantly, provide means to compute statistical guarantees for the correctness of the analysis result.We present the theoretical foundations of our approach and describe two example applications, to the classic k-Tails algorithm and to the recently presented BEAR algorithm.Finally, based on experiments with logs generated from real-world models and with real-world logs provided to us by our industrial partners, we present extensive evidence for the need for scalable log analysis and for the effectiveness of statistical log analysis.
{"title":"Behavioral Log Analysis with Statistical Guarantees","authors":"Nimrod Busany, S. Maoz","doi":"10.1145/2786805.2803198","DOIUrl":"https://doi.org/10.1145/2786805.2803198","url":null,"abstract":"Scalability is a major challenge for existing behavioral log analysis algorithms, which extract finite-state automaton models or temporal properties from logs generated by running systems. In this paper we present statistical log analysis, which addresses scalability using statistical tools. The key to our approach is to consider behavioral log analysis as a statistical experiment.Rather than analyzing the entire log, we suggest to analyze only a sample of traces from the log and, most importantly, provide means to compute statistical guarantees for the correctness of the analysis result.We present the theoretical foundations of our approach and describe two example applications, to the classic k-Tails algorithm and to the recently presented BEAR algorithm.Finally, based on experiments with logs generated from real-world models and with real-world logs provided to us by our industrial partners, we present extensive evidence for the need for scalable log analysis and for the effectiveness of statistical log analysis.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"30 1","pages":"877-887"},"PeriodicalIF":0.0,"publicationDate":"2015-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87752428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Bersani, D. Bianculli, C. Ghezzi, S. Krstic, P. S. Pietro
The problem of checking a logged event trace against a temporal logic specification arises in many practical cases. Unfortunately, known algorithms for an expressive logic like MTL (Metric Temporal Logic) do not scale with respect to two crucial dimensions: the length of the trace and the size of the time interval of the formula to be checked. The former issue can be addressed by distributed and parallel trace checking algorithms that can take advantage of modern cloud computing and programming frameworks like MapReduce. Still, the latter issue remains open with current state-of-the-art approaches. In this paper we address this memory scalability issue by proposing a new semantics for MTL, called lazy semantics. This semantics can evaluate temporal formulae and boolean combinations of temporal-only formulae at any arbitrary time instant. We prove that lazy semantics is more expressive than point-based semantics and that it can be used as a basis for a correct parametric decomposition of any MTL formula into an equivalent one with smaller, bounded time intervals. We use lazy semantics to extend our previous distributed trace checking algorithm for MTL. The evaluation shows that the proposed algorithm can check formulae with large intervals, on large traces, in a memory-efficient way.
{"title":"Efficient Large-Scale Trace Checking Using MapReduce","authors":"M. Bersani, D. Bianculli, C. Ghezzi, S. Krstic, P. S. Pietro","doi":"10.1145/2884781.2884832","DOIUrl":"https://doi.org/10.1145/2884781.2884832","url":null,"abstract":"The problem of checking a logged event trace against a temporal logic specification arises in many practical cases. Unfortunately, known algorithms for an expressive logic like MTL (Metric Temporal Logic) do not scale with respect to two crucial dimensions: the length of the trace and the size of the time interval of the formula to be checked. The former issue can be addressed by distributed and parallel trace checking algorithms that can take advantage of modern cloud computing and programming frameworks like MapReduce. Still, the latter issue remains open with current state-of-the-art approaches. In this paper we address this memory scalability issue by proposing a new semantics for MTL, called lazy semantics. This semantics can evaluate temporal formulae and boolean combinations of temporal-only formulae at any arbitrary time instant. We prove that lazy semantics is more expressive than point-based semantics and that it can be used as a basis for a correct parametric decomposition of any MTL formula into an equivalent one with smaller, bounded time intervals. We use lazy semantics to extend our previous distributed trace checking algorithm for MTL. The evaluation shows that the proposed algorithm can check formulae with large intervals, on large traces, in a memory-efficient way.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"134 1","pages":"888-898"},"PeriodicalIF":0.0,"publicationDate":"2015-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80986839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile app developers rely heavily on standard API frameworks and libraries. However, learning API usages is often challenging due to the fast-changing nature of API frameworks for mobile systems and the insufficiency of API documentation and source code examples. In this paper, we propose a novel approach to learn API usages from bytecode of Android mobile apps. Our core contributions include HAPI, a statistical model of API usages and three algorithms to extract method call sequences from apps’ bytecode, to train HAPI based on those sequences, and to recommend method calls in code completion using the trained HAPIs. Our empirical evaluation shows that our prototype tool can effectively learn API usages from 200 thousand apps containing 350 million method sequences. It recommends next method calls with top-3 accuracy of 90% and outperforms baseline approaches on average 10-20%.
{"title":"Learning API Usages from Bytecode: A Statistical Approach","authors":"Tam The Nguyen, H. Pham, P. Vu, T. Nguyen","doi":"10.1145/2884781.2884873","DOIUrl":"https://doi.org/10.1145/2884781.2884873","url":null,"abstract":"Mobile app developers rely heavily on standard API frameworks and libraries. However, learning API usages is often challenging due to the fast-changing nature of API frameworks for mobile systems and the insufficiency of API documentation and source code examples. In this paper, we propose a novel approach to learn API usages from bytecode of Android mobile apps. Our core contributions include HAPI, a statistical model of API usages and three algorithms to extract method call sequences from apps’ bytecode, to train HAPI based on those sequences, and to recommend method calls in code completion using the trained HAPIs. Our empirical evaluation shows that our prototype tool can effectively learn API usages from 200 thousand apps containing 350 million method sequences. It recommends next method calls with top-3 accuracy of 90% and outperforms baseline approaches on average 10-20%.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"322 1","pages":"416-427"},"PeriodicalIF":0.0,"publicationDate":"2015-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80287215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Baishakhi Ray, V. Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, Premkumar T. Devanbu
Real software, the kind working programmers produce by the kLOC to solve real-world problems, tends to be “natural”, like speech or natural language; it tends to be highly repetitive and predictable. Researchers have captured this naturalness of software through statistical models and used them to good effect in suggestion engines, porting tools, coding standards checkers, and idiom miners. This suggests that code that appears improbable, or surprising, to a good statistical language model is “unnatural” in some sense, and thus possibly suspicious. In this paper, we investigate this hypothesis. We consider a large corpus of bug fix commits (ca. 7,139), from 10 different Java projects, and focus on its language statistics, evaluating the naturalness of buggy code and the corresponding fixes. We find that code with bugs tends to be more entropic (i.e. unnatural), becoming less so as bugs are fixed. Ordering files for inspection by their average entropy yields cost-effectiveness scores comparable to popular defect prediction methods. At a finer granularity, focusing on highly entropic lines is similar in cost-effectiveness to some well-known static bug finders (PMD, FindBugs) and or- dering warnings from these bug finders using an entropy measure improves the cost-effectiveness of inspecting code implicated in warnings. This suggests that entropy may be a valid, simple way to complement the effectiveness of PMD or FindBugs, and that search-based bug-fixing methods may benefit from using entropy both for fault-localization and searching for fixes.
{"title":"On the \"Naturalness\" of Buggy Code","authors":"Baishakhi Ray, V. Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, Premkumar T. Devanbu","doi":"10.1145/2884781.2884848","DOIUrl":"https://doi.org/10.1145/2884781.2884848","url":null,"abstract":"Real software, the kind working programmers produce by the kLOC to solve real-world problems, tends to be “natural”, like speech or natural language; it tends to be highly repetitive and predictable. Researchers have captured this naturalness of software through statistical models and used them to good effect in suggestion engines, porting tools, coding standards checkers, and idiom miners. This suggests that code that appears improbable, or surprising, to a good statistical language model is “unnatural” in some sense, and thus possibly suspicious. In this paper, we investigate this hypothesis. We consider a large corpus of bug fix commits (ca. 7,139), from 10 different Java projects, and focus on its language statistics, evaluating the naturalness of buggy code and the corresponding fixes. We find that code with bugs tends to be more entropic (i.e. unnatural), becoming less so as bugs are fixed. Ordering files for inspection by their average entropy yields cost-effectiveness scores comparable to popular defect prediction methods. At a finer granularity, focusing on highly entropic lines is similar in cost-effectiveness to some well-known static bug finders (PMD, FindBugs) and or- dering warnings from these bug finders using an entropy measure improves the cost-effectiveness of inspecting code implicated in warnings. This suggests that entropy may be a valid, simple way to complement the effectiveness of PMD or FindBugs, and that search-based bug-fixing methods may benefit from using entropy both for fault-localization and searching for fixes.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"62 1","pages":"428-439"},"PeriodicalIF":0.0,"publicationDate":"2015-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85783247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The pull-based development model is an emerging way of contributing to distributed software projects that is gaining enormous popularity within the open source software (OSS) world. Previous work has examined this model by focusing on projects and their owners—we complement it by examining the work practices of project contributors and the challenges they face.We conducted a survey with 645 top contributors to active OSS projects using the pull-based model on GitHub, the prevalent social coding site. We also analyzed traces extracted from corresponding GitHub repositories. Our research shows that: contributors have a strong interest in maintaining awareness of project status to get inspiration and avoid duplicating work, but they do not actively propagate information; communication within pull requests is reportedly limited to low-level concerns and contributors often use communication channels external to pull requests; challenges are mostly social in nature, with most reporting poor responsiveness from integrators; and the increased transparency of this setting is a confirmed motivation to contribute. Based on these findings, we present recommendations for practitioners to streamline the contribution process and discuss potential future research directions.
{"title":"Work Practices and Challenges in Pull-Based Development: The Contributor's Perspective","authors":"Georgios Gousios, M. Storey, Alberto Bacchelli","doi":"10.1145/2884781.2884826","DOIUrl":"https://doi.org/10.1145/2884781.2884826","url":null,"abstract":"The pull-based development model is an emerging way of contributing to distributed software projects that is gaining enormous popularity within the open source software (OSS) world. Previous work has examined this model by focusing on projects and their owners—we complement it by examining the work practices of project contributors and the challenges they face.We conducted a survey with 645 top contributors to active OSS projects using the pull-based model on GitHub, the prevalent social coding site. We also analyzed traces extracted from corresponding GitHub repositories. Our research shows that: contributors have a strong interest in maintaining awareness of project status to get inspiration and avoid duplicating work, but they do not actively propagate information; communication within pull requests is reportedly limited to low-level concerns and contributors often use communication channels external to pull requests; challenges are mostly social in nature, with most reporting poor responsiveness from integrators; and the increased transparency of this setting is a confirmed motivation to contribute. Based on these findings, we present recommendations for practitioners to streamline the contribution process and discuss potential future research directions.","PeriodicalId":6485,"journal":{"name":"2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE)","volume":"3 1","pages":"285-296"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81350533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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