Open source software projects often rely on code contributions from a wide variety of developers to extend the capabilities of their software. Project members evaluate these contributions and often engage in extended discussions to decide whether to integrate changes. These discussions have important implications for project management regarding new contributors and evolution of project requirements and direction. We present a study of how developers in open work environments evaluate and discuss pull requests, a primary method of contribution in GitHub, analyzing a sample of extended discussions around pull requests and interviews with GitHub developers. We found that developers raised issues around contributions over both the appropriateness of the problem that the submitter attempted to solve and the correctness of the implemented solution. Both core project members and third-party stakeholders discussed and sometimes implemented alternative solutions to address these issues. Different stakeholders also influenced the outcome of the evaluation by eliciting support from different communities such as dependent projects or even companies. We also found that evaluation outcomes may be more complex than simply acceptance or rejection. In some cases, although a submitter's contribution was rejected, the core team fulfilled the submitter's technical goals by implementing an alternative solution. We found that the level of a submitter's prior interaction on a project changed how politely developers discussed the contribution and the nature of proposed alternative solutions.
{"title":"Let's talk about it: evaluating contributions through discussion in GitHub","authors":"Jason Tsay, Laura A. Dabbish, J. Herbsleb","doi":"10.1145/2635868.2635882","DOIUrl":"https://doi.org/10.1145/2635868.2635882","url":null,"abstract":"Open source software projects often rely on code contributions from a wide variety of developers to extend the capabilities of their software. Project members evaluate these contributions and often engage in extended discussions to decide whether to integrate changes. These discussions have important implications for project management regarding new contributors and evolution of project requirements and direction. We present a study of how developers in open work environments evaluate and discuss pull requests, a primary method of contribution in GitHub, analyzing a sample of extended discussions around pull requests and interviews with GitHub developers. We found that developers raised issues around contributions over both the appropriateness of the problem that the submitter attempted to solve and the correctness of the implemented solution. Both core project members and third-party stakeholders discussed and sometimes implemented alternative solutions to address these issues. Different stakeholders also influenced the outcome of the evaluation by eliciting support from different communities such as dependent projects or even companies. We also found that evaluation outcomes may be more complex than simply acceptance or rejection. In some cases, although a submitter's contribution was rejected, the core team fulfilled the submitter's technical goals by implementing an alternative solution. We found that the level of a submitter's prior interaction on a project changed how politely developers discussed the contribution and the nature of proposed alternative solutions.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120860715","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}
We present SymJS, a comprehensive framework for automatic testing of client-side JavaScript Web applications. The tool contains a symbolic execution engine for JavaScript, and an automatic event explorer for Web pages. Without any user intervention, SymJS can automatically discover and explore Web events, symbolically execute the associated JavaScript code, refine the execution based on dynamic feedbacks, and produce test cases with high coverage. The symbolic engine contains a symbolic virtual machine, a string-numeric solver, and a symbolic executable DOM model. SymJS's innovations include a novel symbolic virtual machine for JavaScript Web, symbolic+dynamic feedback directed event space exploration, and dynamic taint analysis for enhancing event sequence construction. We illustrate the effectiveness of SymJS on standard JavaScript benchmarks and various real-life Web applications. On average SymJS achieves over 90% line coverage for the benchmark programs, significantly outperforming existing methods.
{"title":"SymJS: automatic symbolic testing of JavaScript web applications","authors":"Guodong Li, Esben Andreasen, Indradeep Ghosh","doi":"10.1145/2635868.2635913","DOIUrl":"https://doi.org/10.1145/2635868.2635913","url":null,"abstract":"We present SymJS, a comprehensive framework for automatic testing of client-side JavaScript Web applications. The tool contains a symbolic execution engine for JavaScript, and an automatic event explorer for Web pages. Without any user intervention, SymJS can automatically discover and explore Web events, symbolically execute the associated JavaScript code, refine the execution based on dynamic feedbacks, and produce test cases with high coverage. The symbolic engine contains a symbolic virtual machine, a string-numeric solver, and a symbolic executable DOM model. SymJS's innovations include a novel symbolic virtual machine for JavaScript Web, symbolic+dynamic feedback directed event space exploration, and dynamic taint analysis for enhancing event sequence construction. We illustrate the effectiveness of SymJS on standard JavaScript benchmarks and various real-life Web applications. On average SymJS achieves over 90% line coverage for the benchmark programs, significantly outperforming existing methods.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"103 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120862225","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}
Uncertainty is becoming more prevalent in the software systems we build, introducing challenges in the way we develop software, especially in software testing. In this work we explore how uncertainty affects software testing, how it is managed currently, and how it could be treated more effectively.
{"title":"Known unknowns: testing in the presence of uncertainty","authors":"Sebastian G. Elbaum, David S. Rosenblum","doi":"10.1145/2635868.2666608","DOIUrl":"https://doi.org/10.1145/2635868.2666608","url":null,"abstract":"Uncertainty is becoming more prevalent in the software systems we build, introducing challenges in the way we develop software, especially in software testing. In this work we explore how uncertainty affects software testing, how it is managed currently, and how it could be treated more effectively.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122561533","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}
Vineeth Kashyap, Kyle Dewey, Ethan A. Kuefner, J. Wagner, Kevin Gibbons, J. Sarracino, Ben Wiedermann, B. Hardekopf
JavaScript is used everywhere from the browser to the server, including desktops and mobile devices. However, the current state of the art in JavaScript static analysis lags far behind that of other languages such as C and Java. Our goal is to help remedy this lack. We describe JSAI, a formally specified, robust abstract interpreter for JavaScript. JSAI uses novel abstract domains to compute a reduced product of type inference, pointer analysis, control-flow analysis, string analysis, and integer and boolean constant propagation. Part of JSAI's novelty is user-configurable analysis sensitivity, i.e., context-, path-, and heap-sensitivity. JSAI is designed to be provably sound with respect to a specific concrete semantics for JavaScript, which has been extensively tested against a commercial JavaScript implementation. We provide a comprehensive evaluation of JSAI's performance and precision using an extensive benchmark suite, including real-world JavaScript applications, machine generated JavaScript code via Emscripten, and browser addons. We use JSAI's configurability to evaluate a large number of analysis sensitivities (some well-known, some novel) and observe some surprising results that go against common wisdom. These results highlight the usefulness of a configurable analysis platform such as JSAI.
{"title":"JSAI: a static analysis platform for JavaScript","authors":"Vineeth Kashyap, Kyle Dewey, Ethan A. Kuefner, J. Wagner, Kevin Gibbons, J. Sarracino, Ben Wiedermann, B. Hardekopf","doi":"10.1145/2635868.2635904","DOIUrl":"https://doi.org/10.1145/2635868.2635904","url":null,"abstract":"JavaScript is used everywhere from the browser to the server, including desktops and mobile devices. However, the current state of the art in JavaScript static analysis lags far behind that of other languages such as C and Java. Our goal is to help remedy this lack. We describe JSAI, a formally specified, robust abstract interpreter for JavaScript. JSAI uses novel abstract domains to compute a reduced product of type inference, pointer analysis, control-flow analysis, string analysis, and integer and boolean constant propagation. Part of JSAI's novelty is user-configurable analysis sensitivity, i.e., context-, path-, and heap-sensitivity. JSAI is designed to be provably sound with respect to a specific concrete semantics for JavaScript, which has been extensively tested against a commercial JavaScript implementation. We provide a comprehensive evaluation of JSAI's performance and precision using an extensive benchmark suite, including real-world JavaScript applications, machine generated JavaScript code via Emscripten, and browser addons. We use JSAI's configurability to evaluate a large number of analysis sensitivities (some well-known, some novel) and observe some surprising results that go against common wisdom. These results highlight the usefulness of a configurable analysis platform such as JSAI.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129761593","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}
A. Shi, A. Gyori, Miloš Gligorić, Andrey Zaytsev, D. Marinov
Regression testing is an important activity but can get expensive for large test suites. Test-suite reduction speeds up regression testing by identifying and removing redundant tests based on a given set of requirements. Traditional research on test-suite reduction is rather diverse but most commonly shares three properties: (1) requirements are defined by a coverage criterion such as statement coverage; (2) the reduced test suite has to satisfy all the requirements as the original test suite; and (3) the quality of the reduced test suites is measured on the software version on which the reduction is performed. These properties make it hard for test engineers to decide how to use reduced test suites. We address all three properties of traditional test-suite reduction: (1) we evaluate test-suite reduction with requirements defined by killed mutants; (2) we evaluate inadequate reduction that does not require reduced test suites to satisfy all the requirements; and (3) we propose evolution-aware metrics that evaluate the quality of the reduced test suites across multiple software versions. Our evaluations allow a more thorough exploration of trade-offs in test-suite reduction, and our evolution-aware metrics show how the quality of reduced test suites can change after the version where the reduction is performed. We compare the trade-offs among various reductions on 18 projects with a total of 261,235 tests over 3,590 commits and a cumulative history spanning 35 years of development. Our results help test engineers make a more informed decision about balancing size, coverage, and fault-detection loss of reduced test suites.
{"title":"Balancing trade-offs in test-suite reduction","authors":"A. Shi, A. Gyori, Miloš Gligorić, Andrey Zaytsev, D. Marinov","doi":"10.1145/2635868.2635921","DOIUrl":"https://doi.org/10.1145/2635868.2635921","url":null,"abstract":"Regression testing is an important activity but can get expensive for large test suites. Test-suite reduction speeds up regression testing by identifying and removing redundant tests based on a given set of requirements. Traditional research on test-suite reduction is rather diverse but most commonly shares three properties: (1) requirements are defined by a coverage criterion such as statement coverage; (2) the reduced test suite has to satisfy all the requirements as the original test suite; and (3) the quality of the reduced test suites is measured on the software version on which the reduction is performed. These properties make it hard for test engineers to decide how to use reduced test suites. We address all three properties of traditional test-suite reduction: (1) we evaluate test-suite reduction with requirements defined by killed mutants; (2) we evaluate inadequate reduction that does not require reduced test suites to satisfy all the requirements; and (3) we propose evolution-aware metrics that evaluate the quality of the reduced test suites across multiple software versions. Our evaluations allow a more thorough exploration of trade-offs in test-suite reduction, and our evolution-aware metrics show how the quality of reduced test suites can change after the version where the reduction is performed. We compare the trade-offs among various reductions on 18 projects with a total of 261,235 tests over 3,590 commits and a cumulative history spanning 35 years of development. Our results help test engineers make a more informed decision about balancing size, coverage, and fault-detection loss of reduced test suites.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126881434","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}
This paper attempts optimal task assignment at the enterprise-level by assigning complexity metrics to the programming tasks and predicting task completion times for each of these tasks based on a machine learning framework that factors in programmer attributes. The framework also considers real-time programmer state by using a simple EEG device to detect programmer mood. A final task assignment is made using a PDTS solver.
{"title":"Software programmer management: a machine learning and human computer interaction framework for optimal task assignment","authors":"Harry Raymond Joseph","doi":"10.1145/2635868.2661684","DOIUrl":"https://doi.org/10.1145/2635868.2661684","url":null,"abstract":"This paper attempts optimal task assignment at the enterprise-level by assigning complexity metrics to the programming tasks and predicting task completion times for each of these tasks based on a machine learning framework that factors in programmer attributes. The framework also considers real-time programmer state by using a simple EEG device to detect programmer mood. A final task assignment is made using a PDTS solver.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132909461","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}
A. Filieri, C. Pasareanu, W. Visser, J. Geldenhuys
Symbolic execution techniques have been proposed recently for the probabilistic analysis of programs. These techniques seek to quantify the likelihood of reaching program events of interest, e.g., assert violations. They have many promising applications but have scalability issues due to high computational demand. To address this challenge, we propose a statistical symbolic execution technique that performs Monte Carlo sampling of the symbolic program paths and uses the obtained information for Bayesian estimation and hypothesis testing with respect to the probability of reaching the target events. To speed up the convergence of the statistical analysis, we propose Informed Sampling, an iterative symbolic execution that first explores the paths that have high statistical significance, prunes them from the state space and guides the execution towards less likely paths. The technique combines Bayesian estimation with a partial exact analysis for the pruned paths leading to provably improved convergence of the statistical analysis. We have implemented statistical symbolic execution with informed sampling in the Symbolic PathFinder tool. We show experimentally that the informed sampling obtains more precise results and converges faster than a purely statistical analysis and may also be more efficient than an exact symbolic analysis. When the latter does not terminate symbolic execution with informed sampling can give meaningful results under the same time and memory limits.
{"title":"Statistical symbolic execution with informed sampling","authors":"A. Filieri, C. Pasareanu, W. Visser, J. Geldenhuys","doi":"10.1145/2635868.2635899","DOIUrl":"https://doi.org/10.1145/2635868.2635899","url":null,"abstract":"Symbolic execution techniques have been proposed recently for the probabilistic analysis of programs. These techniques seek to quantify the likelihood of reaching program events of interest, e.g., assert violations. They have many promising applications but have scalability issues due to high computational demand. To address this challenge, we propose a statistical symbolic execution technique that performs Monte Carlo sampling of the symbolic program paths and uses the obtained information for Bayesian estimation and hypothesis testing with respect to the probability of reaching the target events. To speed up the convergence of the statistical analysis, we propose Informed Sampling, an iterative symbolic execution that first explores the paths that have high statistical significance, prunes them from the state space and guides the execution towards less likely paths. The technique combines Bayesian estimation with a partial exact analysis for the pruned paths leading to provably improved convergence of the statistical analysis. We have implemented statistical symbolic execution with informed sampling in the Symbolic PathFinder tool. We show experimentally that the informed sampling obtains more precise results and converges faster than a purely statistical analysis and may also be more efficient than an exact symbolic analysis. When the latter does not terminate symbolic execution with informed sampling can give meaningful results under the same time and memory limits.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114450401","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}
This paper establishes the concept of "preprocessor dependences" as a foundation for building automated refactoring tools that transform source code containing lexical macros and conditional compilation directives, such as those provided by the C preprocessor. We define a preprocessor dependence graph (PPDG) that models the relationships among macro definitions, macro invocations, and conditional compilation directives in a file--the relationships that must be maintained for the semantics of the C preprocessor to be preserved. For many refactorings, a tool can construct a PPDG from the code before and after it is transformed, then perform a linear-time comparison of the two graphs to determine whether the refactoring will operate correctly in the presence of macros and conditional compilation directives. The proposed technique was implemented in OpenRefactory/C and tested by applying refactorings to GNU Coreutils version 8.21. Empirical results indicate that the technique is effective; it successfully handled refactoring scenarios in which Eclipse CDT, Visual Assist X, and XRefactory all refactored code incorrectly.
{"title":"A foundation for refactoring C with macros","authors":"J. Overbey, Farnaz Behrang, M. Hafiz","doi":"10.1145/2635868.2635908","DOIUrl":"https://doi.org/10.1145/2635868.2635908","url":null,"abstract":"This paper establishes the concept of \"preprocessor dependences\" as a foundation for building automated refactoring tools that transform source code containing lexical macros and conditional compilation directives, such as those provided by the C preprocessor. We define a preprocessor dependence graph (PPDG) that models the relationships among macro definitions, macro invocations, and conditional compilation directives in a file--the relationships that must be maintained for the semantics of the C preprocessor to be preserved. For many refactorings, a tool can construct a PPDG from the code before and after it is transformed, then perform a linear-time comparison of the two graphs to determine whether the refactoring will operate correctly in the presence of macros and conditional compilation directives. The proposed technique was implemented in OpenRefactory/C and tested by applying refactorings to GNU Coreutils version 8.21. Empirical results indicate that the technique is effective; it successfully handled refactoring scenarios in which Eclipse CDT, Visual Assist X, and XRefactory all refactored code incorrectly.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115437542","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}
Spreadsheets are widely used in industry. It is estimated that end-user programmers outnumber regular programmers by a factor of 5. However, spreadsheets are error-prone: several reports exist of companies that have lost big sums of money due to spreadsheet errors. In previous work, spreadsheet smells have proven to be the cause of some of these errors. To that end, we have developed a tool that can apply refactorings to spreadsheet formulas, implementing our previous work on spreadsheet refactoring, which showed that spreadsheet formula smells are very common and that refactorings for them are widely applicable and that refactoring them with a tool is both quicker and less error-prone. Our new tool Bumblebee is able to execute refactorings originating from both these papers, by means of an extensible syntax, and can furthermore apply refactorings on entire groups of formulas, thus improving upon the existing tool RefBook. Finally, BumbleBee can also execute transformations other than refactorings.
{"title":"BumbleBee: a refactoring environment for spreadsheet formulas","authors":"F. Hermans, Danny Dig","doi":"10.1145/2635868.2661673","DOIUrl":"https://doi.org/10.1145/2635868.2661673","url":null,"abstract":"Spreadsheets are widely used in industry. It is estimated that end-user programmers outnumber regular programmers by a factor of 5. However, spreadsheets are error-prone: several reports exist of companies that have lost big sums of money due to spreadsheet errors. In previous work, spreadsheet smells have proven to be the cause of some of these errors. To that end, we have developed a tool that can apply refactorings to spreadsheet formulas, implementing our previous work on spreadsheet refactoring, which showed that spreadsheet formula smells are very common and that refactorings for them are widely applicable and that refactoring them with a tool is both quicker and less error-prone. Our new tool Bumblebee is able to execute refactorings originating from both these papers, by means of an extensible syntax, and can furthermore apply refactorings on entire groups of formulas, thus improving upon the existing tool RefBook. Finally, BumbleBee can also execute transformations other than refactorings.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122737370","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}
Recent studies have shown that sketches and diagrams play an important role in the daily work of software developers. If these visual artifacts are archived, they are often detached from the source code they document, because there is no ad- equate tool support to assist developers in capturing, archiving, and retrieving sketches related to certain source code artifacts. This paper presents SketchLink, a tool that aims at increasing the value of sketches and diagrams created during software development by supporting developers in these tasks. Our prototype implementation provides a web application that employs the camera of smartphones and tablets to capture analog sketches, but can also be used on desktop computers to upload, for instance, computer-generated diagrams. We also implemented a plugin for a Java IDE that embeds the links in Javadoc comments and visualizes them in situ in the source code editor as graphical icons.
{"title":"Linking sketches and diagrams to source code artifacts","authors":"Sebastian Baltes, P. Schmitz, S. Diehl","doi":"10.1145/2635868.2661672","DOIUrl":"https://doi.org/10.1145/2635868.2661672","url":null,"abstract":"Recent studies have shown that sketches and diagrams play an important role in the daily work of software developers. If these visual artifacts are archived, they are often detached from the source code they document, because there is no ad- equate tool support to assist developers in capturing, archiving, and retrieving sketches related to certain source code artifacts. This paper presents SketchLink, a tool that aims at increasing the value of sketches and diagrams created during software development by supporting developers in these tasks. Our prototype implementation provides a web application that employs the camera of smartphones and tablets to capture analog sketches, but can also be used on desktop computers to upload, for instance, computer-generated diagrams. We also implemented a plugin for a Java IDE that embeds the links in Javadoc comments and visualizes them in situ in the source code editor as graphical icons.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123565824","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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