Context: One of the most common artifacts in contemporary software projects is a product backlog comprising user stories, bugs, chores or other work items. However, little research has investigated how the backlog is generated or the precise role it plays in a project. Objective: The purpose of this paper is to determine what is a product backlog, what is its role, and how does it emerge? Method: Following Constructivist Grounded Theory, we conducted a two-year, five-month participant-observation study of eight software development projects at Pivotal, a large, international software company. We interviewed 56 software engineers, product designers, and product managers.We conducted a survey of 27 product designers. We alternated between analysis and theoretical sampling until achieving theoretical saturation. Results: We observed 13 practices and 6 obstacles related to product backlog generation. Limitations: Grounded Theory does not support statistical generalization. While the proposed theory of product backlogs appears widely applicable, organizations with different software development cultures may use different practices. Conclusion: The product backlog is simultaneously a model of work to be done and a boundary object that helps bridge the gap between the processes of generating user stories and realizing them in working code. It emerges from sensemaking (the team making sense of the project context) and coevolution (a cognitive process where the team simultaneously refines its understanding of the problematic context and fledgling solution concepts).
{"title":"The Product Backlog","authors":"Todd Sedano, P. Ralph, Cécile Péraire","doi":"10.1109/ICSE.2019.00036","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00036","url":null,"abstract":"Context: One of the most common artifacts in contemporary software projects is a product backlog comprising user stories, bugs, chores or other work items. However, little research has investigated how the backlog is generated or the precise role it plays in a project. Objective: The purpose of this paper is to determine what is a product backlog, what is its role, and how does it emerge? Method: Following Constructivist Grounded Theory, we conducted a two-year, five-month participant-observation study of eight software development projects at Pivotal, a large, international software company. We interviewed 56 software engineers, product designers, and product managers.We conducted a survey of 27 product designers. We alternated between analysis and theoretical sampling until achieving theoretical saturation. Results: We observed 13 practices and 6 obstacles related to product backlog generation. Limitations: Grounded Theory does not support statistical generalization. While the proposed theory of product backlogs appears widely applicable, organizations with different software development cultures may use different practices. Conclusion: The product backlog is simultaneously a model of work to be done and a boundary object that helps bridge the gap between the processes of generating user stories and realizing them in working code. It emerges from sensemaking (the team making sense of the project context) and coevolution (a cognitive process where the team simultaneously refines its understanding of the problematic context and fledgling solution concepts).","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79392662","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}
H. Tran, Ngoc M. Tran, S. Nguyen, H. Nguyen, T. Nguyen
To avoid the exposure of original source code in a Web application, the variable names in JS code deployed in the wild are often replaced by short, meaningless names, thus making the code extremely difficult to manually understand and analysis. This paper presents JSNeat, an information retrieval (IR)-based approach to recover the variable names in minified JS code. JSNeat follows a data-driven approach to recover names by searching for them in a large corpus of open-source JS code. We use three types of contexts to match a variable in given minified code against the corpus including the context of the properties and roles of the variable, the context of that variable and relations with other variables under recovery, and the context of the task of the function to which the variable contributes. We performed several empirical experiments to evaluate JSNeat on the dataset of more than 322K JS files with 1M functions, and 3.5M variables with 176K unique variable names. We found that JSNeat achieves a high accuracy of 69.1%, which is the relative improvements of 66.1% and 43% over two state-of-the-art approaches JSNice and JSNaughty, respectively. The time to recover for a file or a variable with JSNeat is twice as fast as with JSNice and 4x as fast as with JNaughty, respectively.
{"title":"Recovering Variable Names for Minified Code with Usage Contexts","authors":"H. Tran, Ngoc M. Tran, S. Nguyen, H. Nguyen, T. Nguyen","doi":"10.1109/ICSE.2019.00119","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00119","url":null,"abstract":"To avoid the exposure of original source code in a Web application, the variable names in JS code deployed in the wild are often replaced by short, meaningless names, thus making the code extremely difficult to manually understand and analysis. This paper presents JSNeat, an information retrieval (IR)-based approach to recover the variable names in minified JS code. JSNeat follows a data-driven approach to recover names by searching for them in a large corpus of open-source JS code. We use three types of contexts to match a variable in given minified code against the corpus including the context of the properties and roles of the variable, the context of that variable and relations with other variables under recovery, and the context of the task of the function to which the variable contributes. We performed several empirical experiments to evaluate JSNeat on the dataset of more than 322K JS files with 1M functions, and 3.5M variables with 176K unique variable names. We found that JSNeat achieves a high accuracy of 69.1%, which is the relative improvements of 66.1% and 43% over two state-of-the-art approaches JSNice and JSNaughty, respectively. The time to recover for a file or a variable with JSNeat is twice as fast as with JSNice and 4x as fast as with JNaughty, respectively.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78551787","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}
Authors of papers accepted to the technical track were invited to submit their artifact for evaluation. We received 51 submissions. Each artifact was reviewed by at least two members of our evaluation committee and further assessed in the online discussion phase. If satisfying the criteria, artifacts were awarded one or more badges, which are shown on the front page of their paper and on the conference website.
{"title":"Message from the Artifact Evaluation Chairs of ICSE 2019","authors":"P. Grünbacher, Baishakhi Ray","doi":"10.1109/icse.2019.00009","DOIUrl":"https://doi.org/10.1109/icse.2019.00009","url":null,"abstract":"Authors of papers accepted to the technical track were invited to submit their artifact for evaluation. We received 51 submissions. Each artifact was reviewed by at least two members of our evaluation committee and further assessed in the online discussion phase. If satisfying the criteria, artifacts were awarded one or more badges, which are shown on the front page of their paper and on the conference website.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73740407","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}
Developers often resort to online Q&A forums such as Stack Overflow (SO) for filling their programming needs. Although code examples on those forums are good starting points, they are often incomplete and inadequate for developers' local program contexts; adaptation of those examples is necessary to integrate them to production code. As a consequence, the process of adapting online code examples is done over and over again, by multiple developers independently. Our work extensively studies these adaptations and variations, serving as the basis for a tool that helps integrate these online code examples in a target context in an interactive manner. We perform a large-scale empirical study about the nature and extent of adaptations and variations of SO snippets. We construct a comprehensive dataset linking SO posts to GitHub counterparts based on clone detection, time stamp analysis, and explicit URL references. We then qualitatively inspect 400 SO examples and their GitHub counterparts and develop a taxonomy of 24 adaptation types. Using this taxonomy, we build an automated adaptation analysis technique on top of GumTree to classify the entire dataset into these types. We build a Chrome extension called ExampleStack that automatically lifts an adaptation-aware template from each SO example and its GitHub counterparts to identify hot spots where most changes happen. A user study with sixteen programmers shows that seeing the commonalities and variations in similar GitHub counterparts increases their confidence about the given SO example, and helps them grasp a more comprehensive view about how to reuse the example differently and avoid common pitfalls.
{"title":"Analyzing and Supporting Adaptation of Online Code Examples","authors":"Tianyi Zhang, Di Yang, C. Lopes, Miryung Kim","doi":"10.1109/ICSE.2019.00046","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00046","url":null,"abstract":"Developers often resort to online Q&A forums such as Stack Overflow (SO) for filling their programming needs. Although code examples on those forums are good starting points, they are often incomplete and inadequate for developers' local program contexts; adaptation of those examples is necessary to integrate them to production code. As a consequence, the process of adapting online code examples is done over and over again, by multiple developers independently. Our work extensively studies these adaptations and variations, serving as the basis for a tool that helps integrate these online code examples in a target context in an interactive manner. We perform a large-scale empirical study about the nature and extent of adaptations and variations of SO snippets. We construct a comprehensive dataset linking SO posts to GitHub counterparts based on clone detection, time stamp analysis, and explicit URL references. We then qualitatively inspect 400 SO examples and their GitHub counterparts and develop a taxonomy of 24 adaptation types. Using this taxonomy, we build an automated adaptation analysis technique on top of GumTree to classify the entire dataset into these types. We build a Chrome extension called ExampleStack that automatically lifts an adaptation-aware template from each SO example and its GitHub counterparts to identify hot spots where most changes happen. A user study with sixteen programmers shows that seeing the commonalities and variations in similar GitHub counterparts increases their confidence about the given SO example, and helps them grasp a more comprehensive view about how to reuse the example differently and avoid common pitfalls.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91207748","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}
Many modern applications require low-latency processing of large data sets, often by using approximate algorithms that trade accuracy of the results for faster execution or reduced memory consumption. Although the algorithms provide probabilistic accuracy and performance guarantees, a software developer who implements these algorithms has little support from existing tools. Standard profilers do not consider accuracy of the computation and do not check whether the outputs of these programs satisfy their accuracy specifications. We present AXPROF, an algorithmic profiling framework for analyzing randomized approximate programs. The developer provides the accuracy specification as a formula in a mathematical notation, using probability or expected value predicates. AXPROF automatically generates statistical reasoning code. It first constructs the empirical models of accuracy, time, and memory consumption. It then selects and runs appropriate statistical tests that can, with high confidence, determine if the implementation satisfies the specification. We used AXPROF to profile 15 approximate applications from three domains - data analytics, numerical linear algebra, and approximate computing. AXPROF was effective in finding bugs and identifying various performance optimizations. In particular, we discovered five previously unknown bugs in the implementations of the algorithms and created fixes, guided by AXPROF.
{"title":"Statistical Algorithmic Profiling for Randomized Approximate Programs","authors":"Keyur Joshi, V. Fernando, Sasa Misailovic","doi":"10.1109/ICSE.2019.00071","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00071","url":null,"abstract":"Many modern applications require low-latency processing of large data sets, often by using approximate algorithms that trade accuracy of the results for faster execution or reduced memory consumption. Although the algorithms provide probabilistic accuracy and performance guarantees, a software developer who implements these algorithms has little support from existing tools. Standard profilers do not consider accuracy of the computation and do not check whether the outputs of these programs satisfy their accuracy specifications. We present AXPROF, an algorithmic profiling framework for analyzing randomized approximate programs. The developer provides the accuracy specification as a formula in a mathematical notation, using probability or expected value predicates. AXPROF automatically generates statistical reasoning code. It first constructs the empirical models of accuracy, time, and memory consumption. It then selects and runs appropriate statistical tests that can, with high confidence, determine if the implementation satisfies the specification. We used AXPROF to profile 15 approximate applications from three domains - data analytics, numerical linear algebra, and approximate computing. AXPROF was effective in finding bugs and identifying various performance optimizations. In particular, we discovered five previously unknown bugs in the implementations of the algorithms and created fixes, guided by AXPROF.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91496251","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}
JavaScript is dynamically typed and hence lacks the type safety of statically typed languages, leading to suboptimal IDE support, difficult to understand APIs, and unexpected runtime behavior. Several gradual type systems have been proposed, e.g., Flow and TypeScript, but they rely on developers to annotate code with types. This paper presents NL2Type, a learning-based approach for predicting likely type signatures of JavaScript functions. The key idea is to exploit natural language information in source code, such as comments, function names, and parameter names, a rich source of knowledge that is typically ignored by type inference algorithms. We formulate the problem of predicting types as a classification problem and train a recurrent, LSTM-based neural model that, after learning from an annotated code base, predicts function types for unannotated code. We evaluate the approach with a corpus of 162,673 JavaScript files from real-world projects. NL2Type predicts types with a precision of 84.1% and a recall of 78.9% when considering only the top-most suggestion, and with a precision of 95.5% and a recall of 89.6% when considering the top-5 suggestions. The approach outperforms both JSNice, a state-of-the-art approach that analyzes implementations of functions instead of natural language information, and DeepTyper, a recent type prediction approach that is also based on deep learning. Beyond predicting types, NL2Type serves as a consistency checker for existing type annotations. We show that it discovers 39 inconsistencies that deserve developer attention (from a manual analysis of 50 warnings), most of which are due to incorrect type annotations.
{"title":"NL2Type: Inferring JavaScript Function Types from Natural Language Information","authors":"Rabee Sohail Malik, Jibesh Patra, Michael Pradel","doi":"10.1109/ICSE.2019.00045","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00045","url":null,"abstract":"JavaScript is dynamically typed and hence lacks the type safety of statically typed languages, leading to suboptimal IDE support, difficult to understand APIs, and unexpected runtime behavior. Several gradual type systems have been proposed, e.g., Flow and TypeScript, but they rely on developers to annotate code with types. This paper presents NL2Type, a learning-based approach for predicting likely type signatures of JavaScript functions. The key idea is to exploit natural language information in source code, such as comments, function names, and parameter names, a rich source of knowledge that is typically ignored by type inference algorithms. We formulate the problem of predicting types as a classification problem and train a recurrent, LSTM-based neural model that, after learning from an annotated code base, predicts function types for unannotated code. We evaluate the approach with a corpus of 162,673 JavaScript files from real-world projects. NL2Type predicts types with a precision of 84.1% and a recall of 78.9% when considering only the top-most suggestion, and with a precision of 95.5% and a recall of 89.6% when considering the top-5 suggestions. The approach outperforms both JSNice, a state-of-the-art approach that analyzes implementations of functions instead of natural language information, and DeepTyper, a recent type prediction approach that is also based on deep learning. Beyond predicting types, NL2Type serves as a consistency checker for existing type annotations. We show that it discovers 39 inconsistencies that deserve developer attention (from a manual analysis of 50 warnings), most of which are due to incorrect type annotations.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89358483","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}
Ming Wen, Yepang Liu, Rongxin Wu, Xuan Xie, S. Cheung, Z. Su
Misuses of library APIs are pervasive and often lead to software crashes and vulnerability issues. Various static analysis tools have been proposed to detect library API misuses. They often involve mining frequent patterns from a large number of correct API usage examples, which can be hard to obtain in practice. They also suffer from low precision due to an over-simplified assumption that a deviation from frequent usage patterns indicates a misuse. We make two observations on the discovery of API misuse patterns. First, API misuses can be represented as mutants of the corresponding correct usages. Second, whether a mutant will introduce a misuse can be validated via executing it against a test suite and analyzing the execution information. Based on these observations, we propose MutApi, the first approach to discovering API misuse patterns via mutation analysis. To effectively mimic API misuses based on correct usages, we first design eight effective mutation operators inspired by the common characteristics of API misuses. MutApi generates mutants by applying these mutation operators on a set of client projects and collects mutant-killing tests as well as the associated stack traces. Misuse patterns are discovered from the killed mutants that are prioritized according to their likelihood of causing API misuses based on the collected information. We applied MutApi on 16 client projects with respect to 73 popular Java APIs. The results show that MutApi is able to discover substantial API misuse patterns with a high precision of 0.78. It also achieves a recall of $0.49$ on the MuBench benchmark, which outperforms the state-of-the-art techniques.
{"title":"Exposing Library API Misuses Via Mutation Analysis","authors":"Ming Wen, Yepang Liu, Rongxin Wu, Xuan Xie, S. Cheung, Z. Su","doi":"10.1109/ICSE.2019.00093","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00093","url":null,"abstract":"Misuses of library APIs are pervasive and often lead to software crashes and vulnerability issues. Various static analysis tools have been proposed to detect library API misuses. They often involve mining frequent patterns from a large number of correct API usage examples, which can be hard to obtain in practice. They also suffer from low precision due to an over-simplified assumption that a deviation from frequent usage patterns indicates a misuse. We make two observations on the discovery of API misuse patterns. First, API misuses can be represented as mutants of the corresponding correct usages. Second, whether a mutant will introduce a misuse can be validated via executing it against a test suite and analyzing the execution information. Based on these observations, we propose MutApi, the first approach to discovering API misuse patterns via mutation analysis. To effectively mimic API misuses based on correct usages, we first design eight effective mutation operators inspired by the common characteristics of API misuses. MutApi generates mutants by applying these mutation operators on a set of client projects and collects mutant-killing tests as well as the associated stack traces. Misuse patterns are discovered from the killed mutants that are prioritized according to their likelihood of causing API misuses based on the collected information. We applied MutApi on 16 client projects with respect to 73 popular Java APIs. The results show that MutApi is able to discover substantial API misuse patterns with a high precision of 0.78. It also achieves a recall of $0.49$ on the MuBench benchmark, which outperforms the state-of-the-art techniques.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73085032","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}
Ming Fan, Xiapu Luo, Jun Liu, Meng Wang, Chunyin Nong, Q. Zheng, Ting Liu
The rapid growth of Android malware has posed severe security threats to smartphone users. On the basis of the familial trait of Android malware observed by previous work, the familial analysis is a promising way to help analysts better focus on the commonalities of malware samples within the same families, thus reducing the analytical workload and accelerating malware analysis. The majority of existing approaches rely on supervised learning and face three main challenges, i.e., low accuracy, low efficiency, and the lack of labeled dataset. To address these challenges, we first construct a fine-grained behavior model by abstracting the program semantics into a set of subgraphs. Then, we propose SRA, a novel feature that depicts the similarity relationships between the Structural Roles of sensitive API call nodes in subgraphs. An SRA is obtained based on graph embedding techniques and represented as a vector, thus we can effectively reduce the high complexity of graph matching. After that, instead of training a classifier with labeled samples, we construct malware link network based on SRAs and apply community detection algorithms on it to group the unlabeled samples into groups. We implement these ideas in a system called GefDroid that performs Graph embedding based familial analysis of AnDroid malware using unsupervised learning. Moreover, we conduct extensive experiments to evaluate GefDroid on three datasets with ground truth. The results show that GefDroid can achieve high agreements (0.707-0.883 in term of NMI) between the clustering results and the ground truth. Furthermore, GefDroid requires only linear run-time overhead and takes around 8.6s to analyze a sample on average, which is considerably faster than the previous work.
{"title":"Graph Embedding Based Familial Analysis of Android Malware using Unsupervised Learning","authors":"Ming Fan, Xiapu Luo, Jun Liu, Meng Wang, Chunyin Nong, Q. Zheng, Ting Liu","doi":"10.1109/ICSE.2019.00085","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00085","url":null,"abstract":"The rapid growth of Android malware has posed severe security threats to smartphone users. On the basis of the familial trait of Android malware observed by previous work, the familial analysis is a promising way to help analysts better focus on the commonalities of malware samples within the same families, thus reducing the analytical workload and accelerating malware analysis. The majority of existing approaches rely on supervised learning and face three main challenges, i.e., low accuracy, low efficiency, and the lack of labeled dataset. To address these challenges, we first construct a fine-grained behavior model by abstracting the program semantics into a set of subgraphs. Then, we propose SRA, a novel feature that depicts the similarity relationships between the Structural Roles of sensitive API call nodes in subgraphs. An SRA is obtained based on graph embedding techniques and represented as a vector, thus we can effectively reduce the high complexity of graph matching. After that, instead of training a classifier with labeled samples, we construct malware link network based on SRAs and apply community detection algorithms on it to group the unlabeled samples into groups. We implement these ideas in a system called GefDroid that performs Graph embedding based familial analysis of AnDroid malware using unsupervised learning. Moreover, we conduct extensive experiments to evaluate GefDroid on three datasets with ground truth. The results show that GefDroid can achieve high agreements (0.707-0.883 in term of NMI) between the clustering results and the ground truth. Furthermore, GefDroid requires only linear run-time overhead and takes around 8.6s to analyze a sample on average, which is considerably faster than the previous work.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78961479","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}
{"title":"Program Board of ICSE 2019","authors":"","doi":"10.1109/icse.2019.00013","DOIUrl":"https://doi.org/10.1109/icse.2019.00013","url":null,"abstract":"","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75745949","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}
H. Nguyen, T. Nguyen, Danny Dig, S. Nguyen, H. Tran, Michael C Hilton
Prior research exploited the repetitiveness of code changes to enable several tasks such as code completion, bug-fix recommendation, library adaption, etc. These and other novel applications require accurate detection of semantic changes, but the state-of-the-art methods are limited to algorithms that detect specific kinds of changes at the syntactic level. Existing algorithms relying on syntactic similarity have lower accuracy, and cannot effectively detect semantic change patterns. We introduce a novel graph-based mining approach, CPatMiner, to detect previously unknown repetitive changes in the wild, by mining fine-grained semantic code change patterns from a large number of repositories. To overcome unique challenges such as detecting meaningful change patterns and scaling to large repositories, we rely on fine-grained change graphs to capture program dependencies. We evaluate CPatMiner by mining change patterns in a diverse corpus of 5,000+ open-source projects from GitHub across a population of 170,000+ developers. We use three complementary methods. First, we sent the mined patterns to 108 open-source developers. We found that 70% of respondents recognized those patterns as their meaningful frequent changes. Moreover, 79% of respondents even named the patterns, and 44% wanted future IDEs to automate such repetitive changes. We found that the mined change patterns belong to various development activities: adaptive (9%), perfective (20%), corrective (35%) and preventive (36%, including refactorings). Second, we compared our tool with the state-of-the-art, AST-based technique, and reported that it detects 2.1x more meaningful patterns. Third, we use CPatMiner to search for patterns in a corpus of 88 GitHub projects with longer histories consisting of 164M SLOCs. It constructed 322K fine-grained change graphs containing 3M nodes, and detected 17K instances of change patterns from which we provide unique insights on the practice of change patterns among individuals and teams. We found that a large percentage (75%) of the change patterns from individual developers are commonly shared with others, and this holds true for teams. Moreover, we found that the patterns are not intermittent but spread widely over time. Thus, we call for a community-based change pattern database to provide important resources in novel applications.
{"title":"Graph-Based Mining of In-the-Wild, Fine-Grained, Semantic Code Change Patterns","authors":"H. Nguyen, T. Nguyen, Danny Dig, S. Nguyen, H. Tran, Michael C Hilton","doi":"10.1109/ICSE.2019.00089","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00089","url":null,"abstract":"Prior research exploited the repetitiveness of code changes to enable several tasks such as code completion, bug-fix recommendation, library adaption, etc. These and other novel applications require accurate detection of semantic changes, but the state-of-the-art methods are limited to algorithms that detect specific kinds of changes at the syntactic level. Existing algorithms relying on syntactic similarity have lower accuracy, and cannot effectively detect semantic change patterns. We introduce a novel graph-based mining approach, CPatMiner, to detect previously unknown repetitive changes in the wild, by mining fine-grained semantic code change patterns from a large number of repositories. To overcome unique challenges such as detecting meaningful change patterns and scaling to large repositories, we rely on fine-grained change graphs to capture program dependencies. We evaluate CPatMiner by mining change patterns in a diverse corpus of 5,000+ open-source projects from GitHub across a population of 170,000+ developers. We use three complementary methods. First, we sent the mined patterns to 108 open-source developers. We found that 70% of respondents recognized those patterns as their meaningful frequent changes. Moreover, 79% of respondents even named the patterns, and 44% wanted future IDEs to automate such repetitive changes. We found that the mined change patterns belong to various development activities: adaptive (9%), perfective (20%), corrective (35%) and preventive (36%, including refactorings). Second, we compared our tool with the state-of-the-art, AST-based technique, and reported that it detects 2.1x more meaningful patterns. Third, we use CPatMiner to search for patterns in a corpus of 88 GitHub projects with longer histories consisting of 164M SLOCs. It constructed 322K fine-grained change graphs containing 3M nodes, and detected 17K instances of change patterns from which we provide unique insights on the practice of change patterns among individuals and teams. We found that a large percentage (75%) of the change patterns from individual developers are commonly shared with others, and this holds true for teams. Moreover, we found that the patterns are not intermittent but spread widely over time. Thus, we call for a community-based change pattern database to provide important resources in novel applications.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80854978","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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