Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115725
Saheed A. Busari
Many requirements engineering and software architecture decisions are complicated by uncertainty and multiple conflicting stakeholders objectives. Using quantitative decision models helps clarify these decisions and allows the use of multi-objective simulation optimisation techniques in analysing the impact of decisions on objectives. Existing requirements and architecture decision support methods that use quantitative decision models are limited by the difficulty in elaborating problem-specific decision models and/or lack integrated tool support for automated decision analysis under uncertainty. To address these problems and facilitate requirements and architecture decision analysis, this research proposes a novel modelling language and automated decision analysis technique, implemented in a tool called RADAR. The modelling language is a simplified version of quantitative AND/OR goal models used in requirements engineering and similar to feature models used in software product lines. This research involves developing the RADAR tool and evaluating the tool's applicability, usefulness and scalability on a set of real-world examples.
{"title":"Towards search-based modelling and analysis of requirements and architecture decisions","authors":"Saheed A. Busari","doi":"10.1109/ASE.2017.8115725","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115725","url":null,"abstract":"Many requirements engineering and software architecture decisions are complicated by uncertainty and multiple conflicting stakeholders objectives. Using quantitative decision models helps clarify these decisions and allows the use of multi-objective simulation optimisation techniques in analysing the impact of decisions on objectives. Existing requirements and architecture decision support methods that use quantitative decision models are limited by the difficulty in elaborating problem-specific decision models and/or lack integrated tool support for automated decision analysis under uncertainty. To address these problems and facilitate requirements and architecture decision analysis, this research proposes a novel modelling language and automated decision analysis technique, implemented in a tool called RADAR. The modelling language is a simplified version of quantitative AND/OR goal models used in requirements engineering and similar to feature models used in software product lines. This research involves developing the RADAR tool and evaluating the tool's applicability, usefulness and scalability on a set of real-world examples.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"8 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":"129605930","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.2017.8115658
Gwendal Daniel, F. Jouault, G. Sunyé, Jordi Cabot
Industrial use of Model Driven Engineering techniques has emphasized the need for efficiently store, access, and transform very large models. While scalable persistence frameworks, typically based on some kind of NoSQL database, have been proposed to solve the model storage issue, the same level of performance improvement has not been achieved for the model transformation problem. Existing model transformation tools (such as the well-known ATL) often require the input models to be loaded in memory prior to the start of the transformation and are not optimized to benefit from lazy-loading mechanisms, mainly due to their dependency on current low-level APIs offered by the most popular modeling frameworks nowadays. In this paper we present Gremlin-ATL, a scalable and efficient model-to-model transformation framework that translates ATL transformations into Gremlin, a query language supported by several NoSQL databases. With Gremlin-ATL, the transformation is computed within the database itself, bypassing the modeling framework limitations and improving its performance both in terms of execution time and memory consumption. Tool support is available online.
{"title":"Gremlin-ATL: A scalable model transformation framework","authors":"Gwendal Daniel, F. Jouault, G. Sunyé, Jordi Cabot","doi":"10.1109/ASE.2017.8115658","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115658","url":null,"abstract":"Industrial use of Model Driven Engineering techniques has emphasized the need for efficiently store, access, and transform very large models. While scalable persistence frameworks, typically based on some kind of NoSQL database, have been proposed to solve the model storage issue, the same level of performance improvement has not been achieved for the model transformation problem. Existing model transformation tools (such as the well-known ATL) often require the input models to be loaded in memory prior to the start of the transformation and are not optimized to benefit from lazy-loading mechanisms, mainly due to their dependency on current low-level APIs offered by the most popular modeling frameworks nowadays. In this paper we present Gremlin-ATL, a scalable and efficient model-to-model transformation framework that translates ATL transformations into Gremlin, a query language supported by several NoSQL databases. With Gremlin-ATL, the transformation is computed within the database itself, bypassing the modeling framework limitations and improving its performance both in terms of execution time and memory consumption. Tool support is available online.","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":"125496015","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.2017.8115670
D. Liew, Daniel Schemmel, Cristian Cadar, A. Donaldson, Rafael Zähl, Klaus Wehrle
Symbolic execution is a well-known program analysis technique for testing software, which makes intensive use of constraint solvers. Recent support for floating-point constraint solving has made it feasible to support floating-point reasoning in symbolic execution tools. In this paper, we present the experience of two research teams that independently added floating-point support to KLEE, a popular symbolic execution engine. Since the two teams independently developed their extensions, this created the rare opportunity to conduct a rigorous comparison between the two implementations, essentially a modern case study on N-version programming. As part of our comparison, we report on the different design and implementation decisions taken by each team, and show their impact on a rigorously assembled and tested set of benchmarks, itself a contribution of the paper.
{"title":"Floating-point symbolic execution: A case study in N-version programming","authors":"D. Liew, Daniel Schemmel, Cristian Cadar, A. Donaldson, Rafael Zähl, Klaus Wehrle","doi":"10.1109/ASE.2017.8115670","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115670","url":null,"abstract":"Symbolic execution is a well-known program analysis technique for testing software, which makes intensive use of constraint solvers. Recent support for floating-point constraint solving has made it feasible to support floating-point reasoning in symbolic execution tools. In this paper, we present the experience of two research teams that independently added floating-point support to KLEE, a popular symbolic execution engine. Since the two teams independently developed their extensions, this created the rare opportunity to conduct a rigorous comparison between the two implementations, essentially a modern case study on N-version programming. As part of our comparison, we report on the different design and implementation decisions taken by each team, and show their impact on a rigorously assembled and tested set of benchmarks, itself a contribution of the paper.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"66 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":"128601880","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.2017.8115715
Gias Uddin, Foutse Khomh
Opinions are key determinants to many of the activities related to software development. The perceptions of developers about an API, and the choices they make about whether and how they should use it, may, to a considerable degree, be conditioned upon how other developers see and evaluate the API. Given the plethora of APIs available for a given development task and the advent of developer forums as the media to share opinions about those APIs, it can be challenging for a developer to make informed decisions about an API to support the task. We introduce Opiner, our opinion search and summarization engine for API reviews. The server side of Opiner collects and summarizes opinions about APIs by crawling online developer forums and by associating the opinions found in the forum posts to the APIs discussed in the posts. The client side of Opiner is a Website that presents different summarized viewpoints of the opinions about the APIs in an online search engine. We evaluated Opiner by asking Industrial developers to select APIs for two development tasks. We found that developers were interested to use our proposed summaries of API reviews and that while combined with Stack Overflow, Opiner helped developers to make the right decision with more accuracy and confidence. The Opiner online search engine is available at: http://opiner.polymtl.ca. A video demo is available at: https://youtu.be/XAXpfmg5Lqs.
{"title":"Opiner: An opinion search and summarization engine for APIs","authors":"Gias Uddin, Foutse Khomh","doi":"10.1109/ASE.2017.8115715","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115715","url":null,"abstract":"Opinions are key determinants to many of the activities related to software development. The perceptions of developers about an API, and the choices they make about whether and how they should use it, may, to a considerable degree, be conditioned upon how other developers see and evaluate the API. Given the plethora of APIs available for a given development task and the advent of developer forums as the media to share opinions about those APIs, it can be challenging for a developer to make informed decisions about an API to support the task. We introduce Opiner, our opinion search and summarization engine for API reviews. The server side of Opiner collects and summarizes opinions about APIs by crawling online developer forums and by associating the opinions found in the forum posts to the APIs discussed in the posts. The client side of Opiner is a Website that presents different summarized viewpoints of the opinions about the APIs in an online search engine. We evaluated Opiner by asking Industrial developers to select APIs for two development tasks. We found that developers were interested to use our proposed summaries of API reviews and that while combined with Stack Overflow, Opiner helped developers to make the right decision with more accuracy and confidence. The Opiner online search engine is available at: http://opiner.polymtl.ca. A video demo is available at: https://youtu.be/XAXpfmg5Lqs.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"36 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":"127878807","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.2017.8115636
Jia Chen, Oswaldo Olivo, Işıl Dillig, Calvin Lin
Web applications can leak confidential user information due to the presence of unintended side-channel vulnerabilities in code. One particularly subtle class of side-channel vulnerabilities arises due to resource usage imbalances along different execution paths of a program. Such side-channel vulnerabilities are especially severe if the resource usage imbalance is asymptotic. This paper formalizes the notion of asymptotic resource side-channels and presents a lightweight static analysis algorithm for automatically detecting them. Based on these ideas, we have developed a tool called SCANNER that detects resource-related side-channel vulnerabilities in PHP applications. SCANNER has found 18 zero-day security vulnerabilities in 10 different web applications and reports only 2 false positives. The vulnerabilities uncovered by SCANNER can be exploited using cross-site search attacks to extract various kinds of confidential information, such as a user's medications or purchase history.
{"title":"Static detection of asymptotic resource side-channel vulnerabilities in web applications","authors":"Jia Chen, Oswaldo Olivo, Işıl Dillig, Calvin Lin","doi":"10.1109/ASE.2017.8115636","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115636","url":null,"abstract":"Web applications can leak confidential user information due to the presence of unintended side-channel vulnerabilities in code. One particularly subtle class of side-channel vulnerabilities arises due to resource usage imbalances along different execution paths of a program. Such side-channel vulnerabilities are especially severe if the resource usage imbalance is asymptotic. This paper formalizes the notion of asymptotic resource side-channels and presents a lightweight static analysis algorithm for automatically detecting them. Based on these ideas, we have developed a tool called SCANNER that detects resource-related side-channel vulnerabilities in PHP applications. SCANNER has found 18 zero-day security vulnerabilities in 10 different web applications and reports only 2 false positives. The vulnerabilities uncovered by SCANNER can be exploited using cross-site search attacks to extract various kinds of confidential information, such as a user's medications or purchase history.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"77 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":"121742554","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.2017.8115674
Liushan Chen, Yu Pei, Carlo A. Furia
Automated program repair (APR) is a promising approach to automatically fixing software bugs. Most APR techniques use tests to drive the repair process; this makes them readily applicable to realistic code bases, but also brings the risk of generating spurious repairs that overfit the available tests. Some techniques addressed the overfitting problem by targeting code using contracts (such as pre- and postconditions), which provide additional information helpful to characterize the states of correct and faulty computations; unfortunately, mainstream programming languages do not normally include contract annotations, which severely limits the applicability of such contract-based techniques. This paper presents JAID, a novel APR technique for Java programs, which is capable of constructing detailed state abstractions—similar to those employed by contract-based techniques—that are derived from regular Java code without any special annotations. Grounding the repair generation and validation processes on rich state abstractions mitigates the overfitting problem, and helps extend APR's applicability: in experiments with the DEFECTS4J benchmark, a prototype implementation of JAID produced genuinely correct repairs, equivalent to those written by programmers, for 25 bugs—improving over the state of the art of comparable Java APR techniques in the number and kinds of correct fixes.
{"title":"Contract-based program repair without the contracts","authors":"Liushan Chen, Yu Pei, Carlo A. Furia","doi":"10.1109/ASE.2017.8115674","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115674","url":null,"abstract":"Automated program repair (APR) is a promising approach to automatically fixing software bugs. Most APR techniques use tests to drive the repair process; this makes them readily applicable to realistic code bases, but also brings the risk of generating spurious repairs that overfit the available tests. Some techniques addressed the overfitting problem by targeting code using contracts (such as pre- and postconditions), which provide additional information helpful to characterize the states of correct and faulty computations; unfortunately, mainstream programming languages do not normally include contract annotations, which severely limits the applicability of such contract-based techniques. This paper presents JAID, a novel APR technique for Java programs, which is capable of constructing detailed state abstractions—similar to those employed by contract-based techniques—that are derived from regular Java code without any special annotations. Grounding the repair generation and validation processes on rich state abstractions mitigates the overfitting problem, and helps extend APR's applicability: in experiments with the DEFECTS4J benchmark, a prototype implementation of JAID produced genuinely correct repairs, equivalent to those written by programmers, for 25 bugs—improving over the state of the art of comparable Java APR techniques in the number and kinds of correct fixes.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"12 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":"125789092","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.2017.8115702
Lukas Schmidt, Apurva Narayan, S. Fischmeister
Software specifications are useful for software validation, model checking, runtime verification, debugging, monitoring, etc. In context of safety-critical real-time systems, temporal properties play an important role. However, temporal properties are rarely present due to the complexity and evolutionary nature of software systems. We propose Timed Regular Expression Mining (TREM) a hosted tool for specification mining using timed regular expressions (TREs). It is designed for easy and robust mining of dominant temporal properties. TREM uses an abstract structure of the property; the framework constructs a finite state machine to serve as an acceptor. TREM is scalable, easy to access/use, and platform independent specification mining framework. The tool is tested on industrial strength software system traces such as the QNX real-time operating system using traces with more than 1.5 Million entries. The tool demonstration video can be accessed here: youtu.be/cSd_aj3_LH8
{"title":"TREM: A tool for mining timed regular specifications from system traces","authors":"Lukas Schmidt, Apurva Narayan, S. Fischmeister","doi":"10.1109/ASE.2017.8115702","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115702","url":null,"abstract":"Software specifications are useful for software validation, model checking, runtime verification, debugging, monitoring, etc. In context of safety-critical real-time systems, temporal properties play an important role. However, temporal properties are rarely present due to the complexity and evolutionary nature of software systems. We propose Timed Regular Expression Mining (TREM) a hosted tool for specification mining using timed regular expressions (TREs). It is designed for easy and robust mining of dominant temporal properties. TREM uses an abstract structure of the property; the framework constructs a finite state machine to serve as an acceptor. TREM is scalable, easy to access/use, and platform independent specification mining framework. The tool is tested on industrial strength software system traces such as the QNX real-time operating system using traces with more than 1.5 Million entries. The tool demonstration video can be accessed here: youtu.be/cSd_aj3_LH8","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"15 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":"130435198","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.2017.8115678
Yixiao Yang, Yu Jiang, M. Gu, Jiaguang Sun, Jian Gao, Han Liu
Building language models for source code enables a large set of improvements on traditional software engineering tasks. One promising application is automatic code completion. State-of-the-art techniques capture code regularities at token level with lexical information. Such language models are more suitable for predicting short token sequences, but become less effective with respect to long statement level predictions. In this paper, we have proposed PCC to optimize the token-level based language modeling. Specifically, PCC introduced an intermediate representation (IR) for source code, which puts tokens into groups using lexeme and variable relative order. In this way, PCC is able to handle long token sequences, i.e., group sequences, to suggest a complete statement with the precise synthesizer. Further more, PCC employed a fuzzy matching technique which combined genetic and longest common subsequence algorithms to make the prediction more accurate. We have implemented a code completion plugin for Eclipse and evaluated it on open-source Java projects. The results have demonstrated the potential of PCC in generating precise long statement level predictions. In 30%–60% of the cases, it can correctly suggest the complete statement with only six candidates, and 40%–90% of the cases with ten candidates.
{"title":"A language model for statements of software code","authors":"Yixiao Yang, Yu Jiang, M. Gu, Jiaguang Sun, Jian Gao, Han Liu","doi":"10.1109/ASE.2017.8115678","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115678","url":null,"abstract":"Building language models for source code enables a large set of improvements on traditional software engineering tasks. One promising application is automatic code completion. State-of-the-art techniques capture code regularities at token level with lexical information. Such language models are more suitable for predicting short token sequences, but become less effective with respect to long statement level predictions. In this paper, we have proposed PCC to optimize the token-level based language modeling. Specifically, PCC introduced an intermediate representation (IR) for source code, which puts tokens into groups using lexeme and variable relative order. In this way, PCC is able to handle long token sequences, i.e., group sequences, to suggest a complete statement with the precise synthesizer. Further more, PCC employed a fuzzy matching technique which combined genetic and longest common subsequence algorithms to make the prediction more accurate. We have implemented a code completion plugin for Eclipse and evaluated it on open-source Java projects. The results have demonstrated the potential of PCC in generating precise long statement level predictions. In 30%–60% of the cases, it can correctly suggest the complete statement with only six candidates, and 40%–90% of the cases with ten candidates.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"56 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":"131919370","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.2017.8115656
Lin Shi, Celia Chen, Qing Wang, Shoubin Li, B. Boehm
In open software development environment, a large number of feature requests with mixed quality are often posted by stakeholders and usually managed in issue tracking systems. Thoroughly understanding and analyzing the real intents that feature requests imply is a labor-intensive and challenging task. In this paper, we introduce an approach to understand feature requests automatically. We generate a set of fuzzy rules based on natural language processing techniques that classify each sentence in feature requests into a set of categories: Intent, Explanation, Benefit, Drawback, Example and Trivia. Consequently, the feature requests can be automatically structured based on the classification results. We conduct experiments on 2,112 sentences taken from 602 feature requests of nine popular open source projects. The results show that our method can reach a high performance on classifying sentences from feature requests. Moreover, when applying fuzzy rules on machine learning methods, the performance can be improved significantly.
{"title":"Understanding feature requests by leveraging fuzzy method and linguistic analysis","authors":"Lin Shi, Celia Chen, Qing Wang, Shoubin Li, B. Boehm","doi":"10.1109/ASE.2017.8115656","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115656","url":null,"abstract":"In open software development environment, a large number of feature requests with mixed quality are often posted by stakeholders and usually managed in issue tracking systems. Thoroughly understanding and analyzing the real intents that feature requests imply is a labor-intensive and challenging task. In this paper, we introduce an approach to understand feature requests automatically. We generate a set of fuzzy rules based on natural language processing techniques that classify each sentence in feature requests into a set of categories: Intent, Explanation, Benefit, Drawback, Example and Trivia. Consequently, the feature requests can be automatically structured based on the classification results. We conduct experiments on 2,112 sentences taken from 602 feature requests of nine popular open source projects. The results show that our method can reach a high performance on classifying sentences from feature requests. Moreover, when applying fuzzy rules on machine learning methods, the performance can be improved significantly.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"17 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":"131053738","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.2017.8115688
P. Metzler, Habib Saissi, P. Bokor, N. Suri
The most prominent advantage of software verification over testing is a rigorous check of every possible software behavior. However, large state spaces of concurrent systems, due to non-deterministic scheduling, result in a slow automated verification process. Therefore, verification introduces a large delay between completion and deployment of concurrent software. This paper introduces a novel iterative approach to verification of concurrent programs that drastically reduces this delay. By restricting the execution of concurrent programs to a small set of admissible schedules, verification complexity and time is drastically reduced. Iteratively adding admissible schedules after their verification eventually restores non-deterministic scheduling. Thereby, our framework allows to find a sweet spot between a low verification delay and sufficient execution time performance. Our evaluation of a prototype implementation on well-known benchmark programs shows that after verifying only few schedules of the program, execution time overhead is competitive to existing deterministic multi-threading frameworks.
{"title":"Quick verification of concurrent programs by iteratively relaxed scheduling","authors":"P. Metzler, Habib Saissi, P. Bokor, N. Suri","doi":"10.1109/ASE.2017.8115688","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115688","url":null,"abstract":"The most prominent advantage of software verification over testing is a rigorous check of every possible software behavior. However, large state spaces of concurrent systems, due to non-deterministic scheduling, result in a slow automated verification process. Therefore, verification introduces a large delay between completion and deployment of concurrent software. This paper introduces a novel iterative approach to verification of concurrent programs that drastically reduces this delay. By restricting the execution of concurrent programs to a small set of admissible schedules, verification complexity and time is drastically reduced. Iteratively adding admissible schedules after their verification eventually restores non-deterministic scheduling. Thereby, our framework allows to find a sweet spot between a low verification delay and sufficient execution time performance. Our evaluation of a prototype implementation on well-known benchmark programs shows that after verifying only few schedules of the program, execution time overhead is competitive to existing deterministic multi-threading frameworks.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"81 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":"124098437","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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