Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115704
Christopher Pietsch, Manuel Ohrndorf, U. Kelter, Timo Kehrer
Model slicers are tools which provide two services: (a) finding parts of interest in a model and (b) displaying these parts somehow or extract these parts as a new, autonomous model, which is referred to as slice or sub-model. This paper focuses on the creation of editable slices, which can be processed by model editors, analysis tools, model management tools etc. Slices are useful if, e.g., only a part of a large model shall be analyzed, compared or processed by time-consuming algorithms, or if sub-models shall be modified independently. We present a new generic incremental slicer which can slice models of arbitrary type and which creates slices which are consistent in the sense that they are editable by standard editors. It is built on top of a model differencing framework and does not require additional configuration data beyond those available in the differencing framework. The slicer can incrementally extend or reduce an existing slice if model elements shall be added or removed, even if the slice has been edited meanwhile. We demonstrate the usefulness of our slicer in several scenarios using a large UML model. A screencast of the demonstrated scenarios is provided at http://pi.informatik.uni-siegen.de/projects/SiLift/ase2017.
{"title":"Incrementally slicing editable submodels","authors":"Christopher Pietsch, Manuel Ohrndorf, U. Kelter, Timo Kehrer","doi":"10.1109/ASE.2017.8115704","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115704","url":null,"abstract":"Model slicers are tools which provide two services: (a) finding parts of interest in a model and (b) displaying these parts somehow or extract these parts as a new, autonomous model, which is referred to as slice or sub-model. This paper focuses on the creation of editable slices, which can be processed by model editors, analysis tools, model management tools etc. Slices are useful if, e.g., only a part of a large model shall be analyzed, compared or processed by time-consuming algorithms, or if sub-models shall be modified independently. We present a new generic incremental slicer which can slice models of arbitrary type and which creates slices which are consistent in the sense that they are editable by standard editors. It is built on top of a model differencing framework and does not require additional configuration data beyond those available in the differencing framework. The slicer can incrementally extend or reduce an existing slice if model elements shall be added or removed, even if the slice has been edited meanwhile. We demonstrate the usefulness of our slicer in several scenarios using a large UML model. A screencast of the demonstrated scenarios is provided at http://pi.informatik.uni-siegen.de/projects/SiLift/ase2017.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129231126","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115624
Inderjot Kaur Ratol, M. Robillard
Refactoring is a common software development practice and many simple refactorings can be performed automatically by tools. Identifier renaming is a widely performed refactoring activity. With tool support, rename refactorings can rely on the program structure to ensure correctness of the code transformation. Unfortunately, the textual references to the renamed identifier present in the unstructured comment text cannot be formally detected through the syntax of the language, and are thus fragile with respect to identifier renaming. We designed a new rule-based approach to detect fragile comments. Our approach, called Fraco, takes into account the type of identifier, its morphology, the scope of the identifier and the location of comments. We evaluated the approach by comparing its precision and recall against hand-annotated benchmarks created for six target Java systems, and compared the results against the performance of Eclipse's automated in-comment identifier replacement feature. Fraco performed with near-optimal precision and recall on most components of our evaluation data set, and generally outperformed the baseline Eclipse feature. As part of our evaluation, we also noted that more than half of the total number of identifiers in our data set had fragile comments after renaming, which further motivates the need for research on automatic comment refactoring.
{"title":"Detecting fragile comments","authors":"Inderjot Kaur Ratol, M. Robillard","doi":"10.1109/ASE.2017.8115624","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115624","url":null,"abstract":"Refactoring is a common software development practice and many simple refactorings can be performed automatically by tools. Identifier renaming is a widely performed refactoring activity. With tool support, rename refactorings can rely on the program structure to ensure correctness of the code transformation. Unfortunately, the textual references to the renamed identifier present in the unstructured comment text cannot be formally detected through the syntax of the language, and are thus fragile with respect to identifier renaming. We designed a new rule-based approach to detect fragile comments. Our approach, called Fraco, takes into account the type of identifier, its morphology, the scope of the identifier and the location of comments. We evaluated the approach by comparing its precision and recall against hand-annotated benchmarks created for six target Java systems, and compared the results against the performance of Eclipse's automated in-comment identifier replacement feature. Fraco performed with near-optimal precision and recall on most components of our evaluation data set, and generally outperformed the baseline Eclipse feature. As part of our evaluation, we also noted that more than half of the total number of identifiers in our data set had fragile comments after renaming, which further motivates the need for research on automatic comment refactoring.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133279491","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115698
Ghanem Soltana, M. Sabetzadeh, L. Briand
Usage-based statistical testing employs knowledge about the actual or anticipated usage profile of the system under test for estimating system reliability. For many systems, usage-based statistical testing involves generating synthetic test data. Such data must possess the same statistical characteristics as the actual data that the system will process during operation. Synthetic test data must further satisfy any logical validity constraints that the actual data is subject to. Targeting data-intensive systems, we propose an approach for generating synthetic test data that is both statistically representative and logically valid. The approach works by first generating a data sample that meets the desired statistical characteristics, without taking into account the logical constraints. Subsequently, the approach tweaks the generated sample to fix any logical constraint violations. The tweaking process is iterative and continuously guided toward achieving the desired statistical characteristics. We report on a realistic evaluation of the approach, where we generate a synthetic population of citizens' records for testing a public administration IT system. Results suggest that our approach is scalable and capable of simultaneously fulfilling the statistical representativeness and logical validity requirements.
{"title":"Synthetic data generation for statistical testing","authors":"Ghanem Soltana, M. Sabetzadeh, L. Briand","doi":"10.1109/ASE.2017.8115698","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115698","url":null,"abstract":"Usage-based statistical testing employs knowledge about the actual or anticipated usage profile of the system under test for estimating system reliability. For many systems, usage-based statistical testing involves generating synthetic test data. Such data must possess the same statistical characteristics as the actual data that the system will process during operation. Synthetic test data must further satisfy any logical validity constraints that the actual data is subject to. Targeting data-intensive systems, we propose an approach for generating synthetic test data that is both statistically representative and logically valid. The approach works by first generating a data sample that meets the desired statistical characteristics, without taking into account the logical constraints. Subsequently, the approach tweaks the generated sample to fix any logical constraint violations. The tweaking process is iterative and continuously guided toward achieving the desired statistical characteristics. We report on a realistic evaluation of the approach, where we generate a synthetic population of citizens' records for testing a public administration IT system. Results suggest that our approach is scalable and capable of simultaneously fulfilling the statistical representativeness and logical validity requirements.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127238013","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115720
Jie Wang
JavaScript has become one of the most popular programming languages for both client-side and server-side applications. In JavaScript applications, events may be generated, triggered and consumed non-deterministically. Thus, JavaScript applications may suffer from non-deterministic bugs, when events are triggered and consumed in an unexpected order. In this proposal, we aim to characterize and combat non-deterministic bugs in JavaScript applications. Specifically, we first perform a comprehensive study about real-world non-deterministic bugs in server-side JavaScript applications. In order to facilitate bug diagnosis, we further propose approaches to isolate the necessary events that are responsible for the occurrence of a failure. We also plan to design new techniques in detecting non-deterministic bugs in JavaScript applications.
{"title":"Characterizing and taming non-deterministic bugs in Javascript applications","authors":"Jie Wang","doi":"10.1109/ASE.2017.8115720","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115720","url":null,"abstract":"JavaScript has become one of the most popular programming languages for both client-side and server-side applications. In JavaScript applications, events may be generated, triggered and consumed non-deterministically. Thus, JavaScript applications may suffer from non-deterministic bugs, when events are triggered and consumed in an unexpected order. In this proposal, we aim to characterize and combat non-deterministic bugs in JavaScript applications. Specifically, we first perform a comprehensive study about real-world non-deterministic bugs in server-side JavaScript applications. In order to facilitate bug diagnosis, we further propose approaches to isolate the necessary events that are responsible for the occurrence of a failure. We also plan to design new techniques in detecting non-deterministic bugs in JavaScript applications.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127841044","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115615
Wei Song, Xiangxing Qian, Jeff Huang
With the prevalence of Android-based mobile devices, automated testing for Android apps has received increasing attention. However, owing to the large variety of events that Android supports, test input generation is a challenging task. In this paper, we present a novel approach and an open source tool called EHBDroid for testing Android apps. In contrast to conventional GUI testing approaches, a key novelty of EHBDroid is that it does not generate events from the GUI, but directly invokes callbacks of event handlers. By doing so, EHBDroid can efficiently simulate a large number of events that are difficult to generate by traditional UI-based approaches. We have evaluated EHBDroid on a collection of 35 real-world large-scale Android apps and compared its performance with two state-of-the-art UI-based approaches, Monkey and Dynodroid. Our experimental results show that EHBDroid is significantly more effective and efficient than Monkey and Dynodroid: in a much shorter time, EHBDroid achieves as much as 22.3% higher statement coverage (11.1% on average) than the other two approaches, and found 12 bugs in these benchmarks, including 5 new bugs that the other two failed to find.
{"title":"EHBDroid: Beyond GUI testing for Android applications","authors":"Wei Song, Xiangxing Qian, Jeff Huang","doi":"10.1109/ASE.2017.8115615","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115615","url":null,"abstract":"With the prevalence of Android-based mobile devices, automated testing for Android apps has received increasing attention. However, owing to the large variety of events that Android supports, test input generation is a challenging task. In this paper, we present a novel approach and an open source tool called EHBDroid for testing Android apps. In contrast to conventional GUI testing approaches, a key novelty of EHBDroid is that it does not generate events from the GUI, but directly invokes callbacks of event handlers. By doing so, EHBDroid can efficiently simulate a large number of events that are difficult to generate by traditional UI-based approaches. We have evaluated EHBDroid on a collection of 35 real-world large-scale Android apps and compared its performance with two state-of-the-art UI-based approaches, Monkey and Dynodroid. Our experimental results show that EHBDroid is significantly more effective and efficient than Monkey and Dynodroid: in a much shorter time, EHBDroid achieves as much as 22.3% higher statement coverage (11.1% on average) than the other two approaches, and found 12 bugs in these benchmarks, including 5 new bugs that the other two failed to find.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127916054","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115666
M. Menarini, Yan Yan, W. Griswold
Code changes are often reviewed before they are deployed. Popular source control systems aid code review by presenting textual differences between old and new versions of the code, leaving developers with the difficult task of determining whether the differences actually produced the desired behavior. Fortunately, we can mine such information from code repositories. We propose aiding code review with inter-version semantic differential analysis. During review of a new commit, a developer is presented with summaries of both code differences and behavioral differences, which are expressed as diffs of likely invariants extracted by running the system's test cases. As a result, developers can more easily determine that the code changes produced the desired effect. We created an invariant-mining tool chain, Getty, to support our concept of semantically-assisted code review. To validate our approach, 1) we applied Getty to the commits of 6 popular open source projects, 2) we assessed the performance and cost of running Getty in different configurations, and 3) we performed a comparative user study with 18 developers. Our results demonstrate that semantically-assisted code review is feasible, effective, and that real programmers can leverage it to improve the quality of their reviews.
{"title":"Semantics-assisted code review: An efficient tool chain and a user study","authors":"M. Menarini, Yan Yan, W. Griswold","doi":"10.1109/ASE.2017.8115666","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115666","url":null,"abstract":"Code changes are often reviewed before they are deployed. Popular source control systems aid code review by presenting textual differences between old and new versions of the code, leaving developers with the difficult task of determining whether the differences actually produced the desired behavior. Fortunately, we can mine such information from code repositories. We propose aiding code review with inter-version semantic differential analysis. During review of a new commit, a developer is presented with summaries of both code differences and behavioral differences, which are expressed as diffs of likely invariants extracted by running the system's test cases. As a result, developers can more easily determine that the code changes produced the desired effect. We created an invariant-mining tool chain, Getty, to support our concept of semantically-assisted code review. To validate our approach, 1) we applied Getty to the commits of 6 popular open source projects, 2) we assessed the performance and cost of running Getty in different configurations, and 3) we performed a comparative user study with 18 developers. Our results demonstrate that semantically-assisted code review is feasible, effective, and that real programmers can leverage it to improve the quality of their reviews.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130953098","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115647
Ulf Kargén, N. Shahmehri
Program trace alignment is the process of establishing a correspondence between dynamic instruction instances in executions of two semantically similar but syntactically different programs. In this paper we present what is, to the best of our knowledge, the first method capable of aligning realistically long execution traces of real programs. To maximize generality, our method works entirely on the machine code level, i.e. it does not require access to source code. Moreover, the method is based entirely on dynamic analysis, which avoids the many challenges associated with static analysis of binary code, and which additionally makes our approach inherently resilient to e.g. static code obfuscation. Therefore, we believe that our trace alignment method could prove to be a useful aid in many program analysis tasks, such as debugging, reverse-engineering, investigating plagiarism, and malware analysis. We empirically evaluate our method on 11 popular Linux programs, and show that it is capable of producing meaningful alignments in the presence of various code transformations such as optimization or obfuscation, and that it easily scales to traces with tens of millions of instructions.
{"title":"Towards robust instruction-level trace alignment of binary code","authors":"Ulf Kargén, N. Shahmehri","doi":"10.1109/ASE.2017.8115647","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115647","url":null,"abstract":"Program trace alignment is the process of establishing a correspondence between dynamic instruction instances in executions of two semantically similar but syntactically different programs. In this paper we present what is, to the best of our knowledge, the first method capable of aligning realistically long execution traces of real programs. To maximize generality, our method works entirely on the machine code level, i.e. it does not require access to source code. Moreover, the method is based entirely on dynamic analysis, which avoids the many challenges associated with static analysis of binary code, and which additionally makes our approach inherently resilient to e.g. static code obfuscation. Therefore, we believe that our trace alignment method could prove to be a useful aid in many program analysis tasks, such as debugging, reverse-engineering, investigating plagiarism, and malware analysis. We empirically evaluate our method on 11 popular Linux programs, and show that it is capable of producing meaningful alignments in the presence of various code transformations such as optimization or obfuscation, and that it easily scales to traces with tens of millions of instructions.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122508104","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115691
Thanhvu Nguyen, Matthew B. Dwyer, W. Visser
We introduce a new technique for inferring program invariants that uses symbolic states generated by symbolic execution. Symbolic states, which consist of path conditions and constraints on local variables, are a compact description of sets of concrete program states and they can be used for both invariant inference and invariant verification. Our technique uses a counterexample-based algorithm that creates concrete states from symbolic states, infers candidate invariants from concrete states, and then verifies or refutes candidate invariants using symbolic states. The refutation case produces concrete counterexamples that prevent spurious results and allow the technique to obtain more precise invariants. This process stops when the algorithm reaches a stable set of invariants. We present Symlnfer, a tool that implements these ideas to automatically generate invariants at arbitrary locations in a Java program. The tool obtains symbolic states from Symbolic PathFinder and uses existing algorithms to infer complex (potentially nonlinear) numerical invariants. Our preliminary results show that Symlnfer is effective in using symbolic states to generate precise and useful invariants for proving program safety and analyzing program runtime complexity. We also show that Symlnfer outperforms existing invariant generation systems.
{"title":"Symlnfer: Inferring program invariants using symbolic states","authors":"Thanhvu Nguyen, Matthew B. Dwyer, W. Visser","doi":"10.1109/ASE.2017.8115691","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115691","url":null,"abstract":"We introduce a new technique for inferring program invariants that uses symbolic states generated by symbolic execution. Symbolic states, which consist of path conditions and constraints on local variables, are a compact description of sets of concrete program states and they can be used for both invariant inference and invariant verification. Our technique uses a counterexample-based algorithm that creates concrete states from symbolic states, infers candidate invariants from concrete states, and then verifies or refutes candidate invariants using symbolic states. The refutation case produces concrete counterexamples that prevent spurious results and allow the technique to obtain more precise invariants. This process stops when the algorithm reaches a stable set of invariants. We present Symlnfer, a tool that implements these ideas to automatically generate invariants at arbitrary locations in a Java program. The tool obtains symbolic states from Symbolic PathFinder and uses existing algorithms to infer complex (potentially nonlinear) numerical invariants. Our preliminary results show that Symlnfer is effective in using symbolic states to generate precise and useful invariants for proving program safety and analyzing program runtime complexity. We also show that Symlnfer outperforms existing invariant generation systems.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122678985","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}
Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115668
Kristín Fjóla Tómasdóttir, M. Aniche, A. Deursen
Automatic static analysis tools help developers to automatically spot code issues in their software. They can be of extreme value in languages with dynamic characteristics, such as JavaScript, where developers can easily introduce mistakes which can go unnoticed for a long time, e.g. a simple syntactic or spelling mistake. Although research has already shown how developers perceive such tools for strongly-typed languages such as Java, little is known about their perceptions when it comes to dynamic languages. In this paper, we investigate what motivates and how developers make use of such tools in JavaScript projects. To that goal, we apply a qualitative research method to conduct and analyze a series of 15 interviews with developers responsible for the linter configuration in reputable OSS JavaScript projects that apply the most commonly used linter, ESLint. The results describe the benefits that developers obtain when using ESLint, the different ways one can configure the tool and prioritize its rules, and the existing challenges in applying linters in the real world. These results have direct implications for developers, tool makers, and researchers, such as tool improvements, and a research agenda that aims to increase our knowledge about the usefulness of such analyzers.
{"title":"Why and how JavaScript developers use linters","authors":"Kristín Fjóla Tómasdóttir, M. Aniche, A. Deursen","doi":"10.1109/ASE.2017.8115668","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115668","url":null,"abstract":"Automatic static analysis tools help developers to automatically spot code issues in their software. They can be of extreme value in languages with dynamic characteristics, such as JavaScript, where developers can easily introduce mistakes which can go unnoticed for a long time, e.g. a simple syntactic or spelling mistake. Although research has already shown how developers perceive such tools for strongly-typed languages such as Java, little is known about their perceptions when it comes to dynamic languages. In this paper, we investigate what motivates and how developers make use of such tools in JavaScript projects. To that goal, we apply a qualitative research method to conduct and analyze a series of 15 interviews with developers responsible for the linter configuration in reputable OSS JavaScript projects that apply the most commonly used linter, ESLint. The results describe the benefits that developers obtain when using ESLint, the different ways one can configure the tool and prioritize its rules, and the existing challenges in applying linters in the real world. These results have direct implications for developers, tool makers, and researchers, such as tool improvements, and a research agenda that aims to increase our knowledge about the usefulness of such analyzers.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123514710","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}
Pub Date : 2017-10-30DOI: 10.7287/peerj.preprints.3186v2
M. M. Rahman, C. Roy
During software maintenance, developers usually deal with a significant number of software change requests. As a part of this, they often formulate an initial query from the request texts, and then attempt to map the concepts discussed in the request to relevant source code locations in the software system (a.k.a., concept location). Unfortunately, studies suggest that they often perform poorly in choosing the right search terms for a change task. In this paper, we propose a novel technique-ACER-that takes an initial query, identifies appropriate search terms from the source code using a novel term weight-CodeRank, and then suggests effective reformulation to the initial query by exploiting the source document structures, query quality analysis and machine learning. Experiments with 1,675 baseline queries from eight subject systems report that our technique can improve 71% of the baseline queries which is highly promising. Comparison with five closely related existing techniques in query reformulation not only validates our empirical findings but also demonstrates the superiority of our technique.
{"title":"Improved query reformulation for concept location using CodeRank and document structures","authors":"M. M. Rahman, C. Roy","doi":"10.7287/peerj.preprints.3186v2","DOIUrl":"https://doi.org/10.7287/peerj.preprints.3186v2","url":null,"abstract":"During software maintenance, developers usually deal with a significant number of software change requests. As a part of this, they often formulate an initial query from the request texts, and then attempt to map the concepts discussed in the request to relevant source code locations in the software system (a.k.a., concept location). Unfortunately, studies suggest that they often perform poorly in choosing the right search terms for a change task. In this paper, we propose a novel technique-ACER-that takes an initial query, identifies appropriate search terms from the source code using a novel term weight-CodeRank, and then suggests effective reformulation to the initial query by exploiting the source document structures, query quality analysis and machine learning. Experiments with 1,675 baseline queries from eight subject systems report that our technique can improve 71% of the baseline queries which is highly promising. Comparison with five closely related existing techniques in query reformulation not only validates our empirical findings but also demonstrates the superiority of our technique.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114786992","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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