Pub Date : 2018-03-01DOI: 10.1109/SANER.2018.8330257
B. Dorninger, M. Moser, Albin Kern
Human Machine Interfaces (HMI) play a pivotal role in operating industrial machines. Depending on the extension of a manufacturers domain and the range of its machines as well as the possible options and variants, the ensuing HMI component repository may become substantially large, resulting in significant maintenance requirements and subsequent cost. A combination of cost pressure and other factors, such as significant change of requirements, may then call for a substantial reengineering. A viable alternative to manually reengineering the whole HMI framework might be the use of (semi)-automated reengineering techniques for suitable parts. We describe such a model based reengineering procedure relying on static analysis of the existing source code for suited aspects of a large HMI framework. We will sketch our overall approach including the objectives and highlight some important challenges of transforming HMI component information extracted from source code into a representation developed for the completely redesigned HMI infrastructure in the light of an existing product assembly and configuration process at a large machinery manufacturer.
{"title":"Reengineering an industrial HMI: Approach, objectives, and challenges","authors":"B. Dorninger, M. Moser, Albin Kern","doi":"10.1109/SANER.2018.8330257","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330257","url":null,"abstract":"Human Machine Interfaces (HMI) play a pivotal role in operating industrial machines. Depending on the extension of a manufacturers domain and the range of its machines as well as the possible options and variants, the ensuing HMI component repository may become substantially large, resulting in significant maintenance requirements and subsequent cost. A combination of cost pressure and other factors, such as significant change of requirements, may then call for a substantial reengineering. A viable alternative to manually reengineering the whole HMI framework might be the use of (semi)-automated reengineering techniques for suitable parts. We describe such a model based reengineering procedure relying on static analysis of the existing source code for suited aspects of a large HMI framework. We will sketch our overall approach including the objectives and highlight some important challenges of transforming HMI component information extracted from source code into a representation developed for the completely redesigned HMI infrastructure in the light of an existing product assembly and configuration process at a large machinery manufacturer.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"28 1","pages":"547-551"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75321751","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 : 2018-03-01DOI: 10.1109/SANER.2018.8330254
Andreas Burger, Sten Grüner
Feature identification and localization is a complicated and error-prone task. Nowadays it is mainly done manually by lead software developer or domain experts. Sometimes these experts are no longer available or cannot support in the feature identification and localization process. Due to that we propose a tool which supports this process with an iterative semi-automatic workflow for identifying, localizing and documenting features. Our tool calculates a feature cluster based on an defined entry point that is found by using information retrieval techniques. This feature cluster will be iteratively refined by the user. This iterative feedback-driven workflow enables developer which are not deeply involved in the development of the software to identify and extract features properly. We evaluated our tool on an industrial smart control system for electric motors with first promising results.
{"title":"FINALIsT2: Feature identification, localization, and tracing tool","authors":"Andreas Burger, Sten Grüner","doi":"10.1109/SANER.2018.8330254","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330254","url":null,"abstract":"Feature identification and localization is a complicated and error-prone task. Nowadays it is mainly done manually by lead software developer or domain experts. Sometimes these experts are no longer available or cannot support in the feature identification and localization process. Due to that we propose a tool which supports this process with an iterative semi-automatic workflow for identifying, localizing and documenting features. Our tool calculates a feature cluster based on an defined entry point that is found by using information retrieval techniques. This feature cluster will be iteratively refined by the user. This iterative feedback-driven workflow enables developer which are not deeply involved in the development of the software to identify and extract features properly. We evaluated our tool on an industrial smart control system for electric motors with first promising results.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"58 1","pages":"532-537"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83014684","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 : 2018-03-01DOI: 10.1109/SANER.2018.8330225
Edmilson Campos Neto, D. A. D. Costa, U. Kulesza
SZZ is a widely used algorithm in the software engineering community to identify changes that are likely to introduce bugs (i.e., bug-introducing changes). Despite its wide adoption, SZZ still has room for improvements. For example, current SZZ implementations may still flag refactoring changes as bug-introducing. Refactorings should be disregarded as bug-introducing because they do not change the system behaviour. In this paper, we empirically investigate how refactorings impact both the input (bug-fix changes) and the output (bug-introducing changes) of the SZZ algorithm. We analyse 31,518 issues of ten Apache projects with 20,298 bug-introducing changes. We use an existing tool that automatically detects refactorings in code changes. We observe that 6.5% of lines that are flagged as bug-introducing changes by SZZ are in fact refactoring changes. Regarding bug-fix changes, we observe that 19.9% of lines that are removed during a fix are related to refactorings and, therefore, their respective inducing changes are false positives. We then incorporate the refactoring-detection tool in our Refactoring Aware SZZ Implementation (RA-SZZ). Our results reveal that RA-SZZ reduces 20.8% of the lines that are flagged as bug-introducing changes compared to the state-of-the-art SZZ implementations. Finally, we perform a manual analysis to identify change patterns that are not captured by the refactoring identification tool used in our study. Our results reveal that 47.95% of the analyzed bug-introducing changes contain additional change patterns that RA-SZZ should not flag as bug-introducing.
{"title":"The impact of refactoring changes on the SZZ algorithm: An empirical study","authors":"Edmilson Campos Neto, D. A. D. Costa, U. Kulesza","doi":"10.1109/SANER.2018.8330225","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330225","url":null,"abstract":"SZZ is a widely used algorithm in the software engineering community to identify changes that are likely to introduce bugs (i.e., bug-introducing changes). Despite its wide adoption, SZZ still has room for improvements. For example, current SZZ implementations may still flag refactoring changes as bug-introducing. Refactorings should be disregarded as bug-introducing because they do not change the system behaviour. In this paper, we empirically investigate how refactorings impact both the input (bug-fix changes) and the output (bug-introducing changes) of the SZZ algorithm. We analyse 31,518 issues of ten Apache projects with 20,298 bug-introducing changes. We use an existing tool that automatically detects refactorings in code changes. We observe that 6.5% of lines that are flagged as bug-introducing changes by SZZ are in fact refactoring changes. Regarding bug-fix changes, we observe that 19.9% of lines that are removed during a fix are related to refactorings and, therefore, their respective inducing changes are false positives. We then incorporate the refactoring-detection tool in our Refactoring Aware SZZ Implementation (RA-SZZ). Our results reveal that RA-SZZ reduces 20.8% of the lines that are flagged as bug-introducing changes compared to the state-of-the-art SZZ implementations. Finally, we perform a manual analysis to identify change patterns that are not captured by the refactoring identification tool used in our study. Our results reveal that 47.95% of the analyzed bug-introducing changes contain additional change patterns that RA-SZZ should not flag as bug-introducing.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"145 5 1","pages":"380-390"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89362843","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 : 2018-03-01DOI: 10.1109/SANER.2018.8330222
Deborah S. Katz, Jason Ruchti, Eric Schulte
Decompilation, recovering source code from binary, is useful in many situations where it is necessary to analyze or understand software for which source code is not available. Source code is much easier for humans to read than binary code, and there are many tools available to analyze source code. Existing decompilation techniques often generate source code that is difficult for humans to understand because the generated code often does not use the coding idioms that programmers use. Differences from human-written code also reduce the effectiveness of analysis tools on the decompiled source code. To address the problem of differences between decompiled code and human-written code, we present a novel technique for decompiling binary code snippets using a model based on Recurrent Neural Networks. The model learns properties and patterns that occur in source code and uses them to produce decompilation output. We train and evaluate our technique on snippets of binary machine code compiled from C source code. The general approach we outline in this paper is not language-specific and requires little or no domain knowledge of a language and its properties or how a compiler operates, making the approach easily extensible to new languages and constructs. Furthermore, the technique can be extended and applied in situations to which traditional decompilers are not targeted, such as for decompilation of isolated binary snippets; fast, on-demand decompilation; domain-specific learned decompilation; optimizing for readability of decompilation; and recovering control flow constructs, comments, and variable or function names. We show that the translations produced by this technique are often accurate or close and can provide a useful picture of the snippet's behavior.
{"title":"Using recurrent neural networks for decompilation","authors":"Deborah S. Katz, Jason Ruchti, Eric Schulte","doi":"10.1109/SANER.2018.8330222","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330222","url":null,"abstract":"Decompilation, recovering source code from binary, is useful in many situations where it is necessary to analyze or understand software for which source code is not available. Source code is much easier for humans to read than binary code, and there are many tools available to analyze source code. Existing decompilation techniques often generate source code that is difficult for humans to understand because the generated code often does not use the coding idioms that programmers use. Differences from human-written code also reduce the effectiveness of analysis tools on the decompiled source code. To address the problem of differences between decompiled code and human-written code, we present a novel technique for decompiling binary code snippets using a model based on Recurrent Neural Networks. The model learns properties and patterns that occur in source code and uses them to produce decompilation output. We train and evaluate our technique on snippets of binary machine code compiled from C source code. The general approach we outline in this paper is not language-specific and requires little or no domain knowledge of a language and its properties or how a compiler operates, making the approach easily extensible to new languages and constructs. Furthermore, the technique can be extended and applied in situations to which traditional decompilers are not targeted, such as for decompilation of isolated binary snippets; fast, on-demand decompilation; domain-specific learned decompilation; optimizing for readability of decompilation; and recovering control flow constructs, comments, and variable or function names. We show that the translations produced by this technique are often accurate or close and can provide a useful picture of the snippet's behavior.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"22 1","pages":"346-356"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74606406","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 : 2018-03-01DOI: 10.1109/SANER.2018.8330251
Aleieldin Salem
GoldRusher is a dynamic analysis tool primarily meant to aid reverse engineers with analyzing malware. Based on the fact that hidden code segments rarely execute, the tool is able to rapidly highlight functions and basic blocks that are potentially hidden, and identify the trigger conditions that control their executions.
{"title":"GoldRusher: A miner for rapid identification of hidden code","authors":"Aleieldin Salem","doi":"10.1109/SANER.2018.8330251","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330251","url":null,"abstract":"GoldRusher is a dynamic analysis tool primarily meant to aid reverse engineers with analyzing malware. Based on the fact that hidden code segments rarely execute, the tool is able to rapidly highlight functions and basic blocks that are potentially hidden, and identify the trigger conditions that control their executions.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"42 1","pages":"517-521"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80680942","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 : 2018-03-01DOI: 10.1109/SANER.2018.8330250
Tijana Vislavski, Gordana Rakic, Nicolás Cardozo, Z. Budimac
Code clones mostly have been proven harmful for the development and maintenance of software systems, leading to code deterioration and an increase in bugs as the system evolves. Modern software systems are composed of several components, incorporating multiple technologies in their development. In such systems, it is common to replicate (parts of) functionality across the different components, potentially in a different programming language. Effect of these duplicates is more acute, as their identification becomes more challenging. This paper presents LICCA, a tool for the identification of duplicate code fragments across multiple languages. LICCA is integrated with the SSQSA platform and relies on its high-level representation of code in which it is possible to extract syntactic and semantic characteristics of code fragments positing full cross-language clone detection. LICCA is on a technology development level. We demonstrate its potential by adopting a set of cloning scenarios, extended and rewritten in five characteristic languages: Java, C, JavaScript, Modula-2 and Scheme.
{"title":"LICCA: A tool for cross-language clone detection","authors":"Tijana Vislavski, Gordana Rakic, Nicolás Cardozo, Z. Budimac","doi":"10.1109/SANER.2018.8330250","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330250","url":null,"abstract":"Code clones mostly have been proven harmful for the development and maintenance of software systems, leading to code deterioration and an increase in bugs as the system evolves. Modern software systems are composed of several components, incorporating multiple technologies in their development. In such systems, it is common to replicate (parts of) functionality across the different components, potentially in a different programming language. Effect of these duplicates is more acute, as their identification becomes more challenging. This paper presents LICCA, a tool for the identification of duplicate code fragments across multiple languages. LICCA is integrated with the SSQSA platform and relies on its high-level representation of code in which it is possible to extract syntactic and semantic characteristics of code fragments positing full cross-language clone detection. LICCA is on a technology development level. We demonstrate its potential by adopting a set of cloning scenarios, extended and rewritten in five characteristic languages: Java, C, JavaScript, Modula-2 and Scheme.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"15 24 1","pages":"512-516"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79619888","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 : 2018-03-01DOI: 10.1109/SANER.2018.8330260
Jie Liang, Mingzhe Wang, Yuanliang Chen, Yu Jiang, Renwei Zhang
Fuzz testing has helped security researchers and organizations discover a large number of vulnerabilities. Although it is efficient and widely used in industry, hardly any empirical studies and experience exist on the customization of fuzzers to real industrial projects. In this paper, collaborating with the engineers from Huawei, we present the practice of adapting fuzz testing to a proprietary message middleware named libmsg, which is responsible for the message transfer of the entire distributed system department. We present the main obstacles coming across in applying an efficient fuzzer to libmsg, including system configuration inconsistency, system build complexity, fuzzing driver absence. The solutions for those typical obstacles are also provided. For example, for the most difficult and expensive obstacle of writing fuzzing drivers, we present a low-cost approach by converting existing sample code snippets into fuzzing drivers. After overcoming those obstacles, we can effectively identify software bugs, and report 9 previously unknown vulnerabilities, including flaws that lead to denial of service or system crash.
{"title":"Fuzz testing in practice: Obstacles and solutions","authors":"Jie Liang, Mingzhe Wang, Yuanliang Chen, Yu Jiang, Renwei Zhang","doi":"10.1109/SANER.2018.8330260","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330260","url":null,"abstract":"Fuzz testing has helped security researchers and organizations discover a large number of vulnerabilities. Although it is efficient and widely used in industry, hardly any empirical studies and experience exist on the customization of fuzzers to real industrial projects. In this paper, collaborating with the engineers from Huawei, we present the practice of adapting fuzz testing to a proprietary message middleware named libmsg, which is responsible for the message transfer of the entire distributed system department. We present the main obstacles coming across in applying an efficient fuzzer to libmsg, including system configuration inconsistency, system build complexity, fuzzing driver absence. The solutions for those typical obstacles are also provided. For example, for the most difficult and expensive obstacle of writing fuzzing drivers, we present a low-cost approach by converting existing sample code snippets into fuzzing drivers. After overcoming those obstacles, we can effectively identify software bugs, and report 9 previously unknown vulnerabilities, including flaws that lead to denial of service or system crash.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"22 1","pages":"562-566"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88153900","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 : 2018-03-01DOI: 10.1109/SANER.2018.8330216
Sergio Mover, S. Sankaranarayanan, Rhys Braginton Pettee Olsen, B. E. Chang
We investigate the problem of mining graph-based usage patterns for large, object-oriented frameworks like Android—revisiting previous approaches based on graph-based object usage models (groums). Groums are a promising approach to represent usage patterns for object-oriented libraries because they simultaneously describe control flow and data dependencies between methods of multiple interacting object types. However, this expressivity comes at a cost: mining groums requires solving a subgraph isomorphism problem that is well known to be expensive. This cost limits the applicability of groum mining to large API frameworks. In this paper, we employ groum mining to learn usage patterns for object-oriented frameworks from program corpora. The central challenge is to scale groum mining so that it is sensitive to usages horizontally across programs from arbitrarily many developers (as opposed to simply usages vertically within the program of a single developer). To address this challenge, we develop a novel groum mining algorithm that scales on a large corpus of programs. We first use frequent itemset mining to restrict the search for groums to smaller subsets of methods in the given corpus. Then, we pose the subgraph isomorphism as a SAT problem and apply efficient pre-processing algorithms to rule out fruitless comparisons ahead of time. Finally, we identify containment relationships between clusters of groums to characterize popular usage patterns in the corpus (as well as classify less popular patterns as possible anomalies). We find that our approach scales on a corpus of over five hundred open source Android applications, effectively mining obligatory and best-practice usage patterns.
{"title":"Mining framework usage graphs from app corpora","authors":"Sergio Mover, S. Sankaranarayanan, Rhys Braginton Pettee Olsen, B. E. Chang","doi":"10.1109/SANER.2018.8330216","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330216","url":null,"abstract":"We investigate the problem of mining graph-based usage patterns for large, object-oriented frameworks like Android—revisiting previous approaches based on graph-based object usage models (groums). Groums are a promising approach to represent usage patterns for object-oriented libraries because they simultaneously describe control flow and data dependencies between methods of multiple interacting object types. However, this expressivity comes at a cost: mining groums requires solving a subgraph isomorphism problem that is well known to be expensive. This cost limits the applicability of groum mining to large API frameworks. In this paper, we employ groum mining to learn usage patterns for object-oriented frameworks from program corpora. The central challenge is to scale groum mining so that it is sensitive to usages horizontally across programs from arbitrarily many developers (as opposed to simply usages vertically within the program of a single developer). To address this challenge, we develop a novel groum mining algorithm that scales on a large corpus of programs. We first use frequent itemset mining to restrict the search for groums to smaller subsets of methods in the given corpus. Then, we pose the subgraph isomorphism as a SAT problem and apply efficient pre-processing algorithms to rule out fruitless comparisons ahead of time. Finally, we identify containment relationships between clusters of groums to characterize popular usage patterns in the corpus (as well as classify less popular patterns as possible anomalies). We find that our approach scales on a corpus of over five hundred open source Android applications, effectively mining obligatory and best-practice usage patterns.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"18 1","pages":"277-289"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87567963","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 : 2018-02-21DOI: 10.1109/SANER.2018.8330262
Rodrigo F. Silva, K. V. R. Paixão, M. Maia
Stack Overflow has become a fundamental element of developer toolset. Such influence increase has been accompanied by an effort from Stack Overflow community to keep the quality of its content. One of the problems which jeopardizes that quality is the continuous growth of duplicated questions. To solve this problem, prior works focused on automatically detecting duplicated questions. Two important solutions are DupPredictor and Dupe. Despite reporting significant results, both works do not provide their implementations publicly available, hindering subsequent works in scientific literature which rely on them. We executed an empirical study as a reproduction of DupPredictor and Dupe. Our results, not robust when attempted with different set of tools and data sets, show that the barriers to reproduce these approaches are high. Furthermore, when applied to more recent data, we observe a performance decay of our both reproductions in terms of recall-rate over time, as the number of questions increases. Our findings suggest that the subsequent works concerning detection of duplicated questions in Question and Answer communities require more investigation to assert their findings.
{"title":"Duplicate question detection in stack overflow: A reproducibility study","authors":"Rodrigo F. Silva, K. V. R. Paixão, M. Maia","doi":"10.1109/SANER.2018.8330262","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330262","url":null,"abstract":"Stack Overflow has become a fundamental element of developer toolset. Such influence increase has been accompanied by an effort from Stack Overflow community to keep the quality of its content. One of the problems which jeopardizes that quality is the continuous growth of duplicated questions. To solve this problem, prior works focused on automatically detecting duplicated questions. Two important solutions are DupPredictor and Dupe. Despite reporting significant results, both works do not provide their implementations publicly available, hindering subsequent works in scientific literature which rely on them. We executed an empirical study as a reproduction of DupPredictor and Dupe. Our results, not robust when attempted with different set of tools and data sets, show that the barriers to reproduce these approaches are high. Furthermore, when applied to more recent data, we observe a performance decay of our both reproductions in terms of recall-rate over time, as the number of questions increases. Our findings suggest that the subsequent works concerning detection of duplicated questions in Question and Answer communities require more investigation to assert their findings.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"1 1","pages":"572-581"},"PeriodicalIF":0.0,"publicationDate":"2018-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87099512","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 : 2018-01-19DOI: 10.1109/SANER.2018.8330203
Victor Sobreira, Thomas Durieux, Fernanda Madeiral Delfim, Monperrus Martin, M. Maia
Well-designed and publicly available datasets of bugs are an invaluable asset to advance research fields such as fault localization and program repair as they allow directly and fairly comparison between competing techniques and also the replication of experiments. These datasets need to be deeply understood by researchers: the answer for questions like "which bugs can my technique handle?" and "for which bugs is my technique effective?" depends on the comprehension of properties related to bugs and their patches. However, such properties are usually not included in the datasets, and there is still no widely adopted methodology for characterizing bugs and patches. In this work, we deeply study 395 patches of the Defects4J dataset. Quantitative properties (patch size and spreading) were automatically extracted, whereas qualitative ones (repair actions and patterns) were manually extracted using a thematic analysis-based approach. We found that 1) the median size of Defects4J patches is four lines, and almost 30% of the patches contain only addition of lines; 2) 92% of the patches change only one file, and 38% has no spreading at all; 3) the top-3 most applied repair actions are addition of method calls, conditionals, and assignments, occurring in 77% of the patches; and 4) nine repair patterns were found for 95% of the patches, where the most prevalent, appearing in 43% of the patches, is on conditional blocks. These results are useful for researchers to perform advanced analysis on their techniques' results based on Defects4J. Moreover, our set of properties can be used to characterize and compare different bug datasets.
{"title":"Dissection of a bug dataset: Anatomy of 395 patches from Defects4J","authors":"Victor Sobreira, Thomas Durieux, Fernanda Madeiral Delfim, Monperrus Martin, M. Maia","doi":"10.1109/SANER.2018.8330203","DOIUrl":"https://doi.org/10.1109/SANER.2018.8330203","url":null,"abstract":"Well-designed and publicly available datasets of bugs are an invaluable asset to advance research fields such as fault localization and program repair as they allow directly and fairly comparison between competing techniques and also the replication of experiments. These datasets need to be deeply understood by researchers: the answer for questions like \"which bugs can my technique handle?\" and \"for which bugs is my technique effective?\" depends on the comprehension of properties related to bugs and their patches. However, such properties are usually not included in the datasets, and there is still no widely adopted methodology for characterizing bugs and patches. In this work, we deeply study 395 patches of the Defects4J dataset. Quantitative properties (patch size and spreading) were automatically extracted, whereas qualitative ones (repair actions and patterns) were manually extracted using a thematic analysis-based approach. We found that 1) the median size of Defects4J patches is four lines, and almost 30% of the patches contain only addition of lines; 2) 92% of the patches change only one file, and 38% has no spreading at all; 3) the top-3 most applied repair actions are addition of method calls, conditionals, and assignments, occurring in 77% of the patches; and 4) nine repair patterns were found for 95% of the patches, where the most prevalent, appearing in 43% of the patches, is on conditional blocks. These results are useful for researchers to perform advanced analysis on their techniques' results based on Defects4J. Moreover, our set of properties can be used to characterize and compare different bug datasets.","PeriodicalId":6602,"journal":{"name":"2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"47 1","pages":"130-140"},"PeriodicalIF":0.0,"publicationDate":"2018-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88682623","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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