Software developers spent 20% of their time on information seeking on Stack Overflow, YouTube or an API reference documentation. Software developers can search within Stack Overflow for duplicates or similar posts. They can also take a look on software development documentations that have similar and additional information included as a Stack Overflow post or a development screencast in order to get new inspirations on how to solve their current development problem. The linkage of same and different types of software development documentation might safe time to evolve new software solutions and might increase the productivity of the developer’s work day. In this paper we will discuss our approach to get a broader understanding of different similarity types (exact, similar and maybe) within and between software documentation as well as an understanding of how different software documentations can be extended.
{"title":"On the similarity of software development documentation","authors":"Mathias Ellmann","doi":"10.1145/3106237.3119875","DOIUrl":"https://doi.org/10.1145/3106237.3119875","url":null,"abstract":"Software developers spent 20% of their time on information seeking on Stack Overflow, YouTube or an API reference documentation. Software developers can search within Stack Overflow for duplicates or similar posts. They can also take a look on software development documentations that have similar and additional information included as a Stack Overflow post or a development screencast in order to get new inspirations on how to solve their current development problem. The linkage of same and different types of software development documentation might safe time to evolve new software solutions and might increase the productivity of the developer’s work day. In this paper we will discuss our approach to get a broader understanding of different similarity types (exact, similar and maybe) within and between software documentation as well as an understanding of how different software documentations can be extended.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125821482","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}
Mutation testing of a test suite and a program provides a way to measure the quality of the test suite. In essence, mutation testing is a form of sensitivity testing: by running mutated versions of the program against the test suite, mutation testing measures the suite's sensitivity for detecting bugs that a programmer might introduce into the program. This paper introduces a technique to improve mutation testing that we call wild-caught mutants; it provides a method for creating potential faults that are more closely coupled with changes made by actual programmers. This technique allows the mutation tester to have more certainty that the test suite is sensitive to the kind of changes that have been observed to have been made by programmers in real-world cases.
{"title":"The care and feeding of wild-caught mutants","authors":"D. Brown, Michael Vaughn, B. Liblit, T. Reps","doi":"10.1145/3106237.3106280","DOIUrl":"https://doi.org/10.1145/3106237.3106280","url":null,"abstract":"Mutation testing of a test suite and a program provides a way to measure the quality of the test suite. In essence, mutation testing is a form of sensitivity testing: by running mutated versions of the program against the test suite, mutation testing measures the suite's sensitivity for detecting bugs that a programmer might introduce into the program. This paper introduces a technique to improve mutation testing that we call wild-caught mutants; it provides a method for creating potential faults that are more closely coupled with changes made by actual programmers. This technique allows the mutation tester to have more certainty that the test suite is sensitive to the kind of changes that have been observed to have been made by programmers in real-world cases.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126734656","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}
Xiaofei Xie, Bihuan Chen, Liang Zou, Shang-Wei Lin, Yang Liu, Xiaohong Li
Loop termination is an important problem for proving the correctness of a system and ensuring that the system always reacts. Existing loop termination analysis techniques mainly depend on the synthesis of ranking functions, which is often expensive. In this paper, we present a novel approach, named Loopster, which performs an efficient static analysis to decide the termination for loops based on path termination analysis and path dependency reasoning. Loopster adopts a divide-and-conquer approach: (1) we extract individual paths from a target multi-path loop and analyze the termination of each path, (2) analyze the dependencies between each two paths, and then (3) determine the overall termination of the target loop based on the relations among paths. We evaluate Loopster by applying it on the loop termination competition benchmark and three real-world projects. The results show that Loopster is effective in a majority of loops with better accuracy and 20 ×+ performance improvement compared to the state-of-the-art tools.
{"title":"Loopster: static loop termination analysis","authors":"Xiaofei Xie, Bihuan Chen, Liang Zou, Shang-Wei Lin, Yang Liu, Xiaohong Li","doi":"10.1145/3106237.3106260","DOIUrl":"https://doi.org/10.1145/3106237.3106260","url":null,"abstract":"Loop termination is an important problem for proving the correctness of a system and ensuring that the system always reacts. Existing loop termination analysis techniques mainly depend on the synthesis of ranking functions, which is often expensive. In this paper, we present a novel approach, named Loopster, which performs an efficient static analysis to decide the termination for loops based on path termination analysis and path dependency reasoning. Loopster adopts a divide-and-conquer approach: (1) we extract individual paths from a target multi-path loop and analyze the termination of each path, (2) analyze the dependencies between each two paths, and then (3) determine the overall termination of the target loop based on the relations among paths. We evaluate Loopster by applying it on the loop termination competition benchmark and three real-world projects. The results show that Loopster is effective in a majority of loops with better accuracy and 20 ×+ performance improvement compared to the state-of-the-art tools.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a Bayesian framework for learning probabilistic specifications from large, unstructured code corpora, and then using these specifications to statically detect anomalous, hence likely buggy, program behavior. Our key insight is to build a statistical model that correlates all specifications hidden inside a corpus with the syntax and observed behavior of programs that implement these specifications. During the analysis of a particular program, this model is conditioned into a posterior distribution that prioritizes specifications that are relevant to the program. The problem of finding anomalies is now framed quantitatively, as a problem of computing a distance between a "reference distribution" over program behaviors that our model expects from the program, and the distribution over behaviors that the program actually produces. We implement our ideas in a system, called Salento, for finding anomalous API usage in Android programs. Salento learns specifications using a combination of a topic model and a neural network model. Our encouraging experimental results show that the system can automatically discover subtle errors in Android applications in the wild, and has high precision and recall compared to competing probabilistic approaches.
{"title":"Bayesian specification learning for finding API usage errors","authors":"V. Murali, Swarat Chaudhuri, C. Jermaine","doi":"10.1145/3106237.3106284","DOIUrl":"https://doi.org/10.1145/3106237.3106284","url":null,"abstract":"We present a Bayesian framework for learning probabilistic specifications from large, unstructured code corpora, and then using these specifications to statically detect anomalous, hence likely buggy, program behavior. Our key insight is to build a statistical model that correlates all specifications hidden inside a corpus with the syntax and observed behavior of programs that implement these specifications. During the analysis of a particular program, this model is conditioned into a posterior distribution that prioritizes specifications that are relevant to the program. The problem of finding anomalies is now framed quantitatively, as a problem of computing a distance between a \"reference distribution\" over program behaviors that our model expects from the program, and the distribution over behaviors that the program actually produces. We implement our ideas in a system, called Salento, for finding anomalous API usage in Android programs. Salento learns specifications using a combination of a topic model and a neural network model. Our encouraging experimental results show that the system can automatically discover subtle errors in Android applications in the wild, and has high precision and recall compared to competing probabilistic approaches.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130660582","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}
Jeho Oh, D. Batory, Margaret Myers, Norbert Siegmund
Software Product Lines (SPLs) are highly configurable systems. This raises the challenge to find optimal performing configurations for an anticipated workload. As SPL configuration spaces are huge, it is infeasible to benchmark all configurations to find an optimal one. Prior work focused on building performance models to predict and optimize SPL configurations. Instead, we randomly sample and recursively search a configuration space directly to find near-optimal configurations without constructing a prediction model. Our algorithms are simpler and have higher accuracy and efficiency.
{"title":"Finding near-optimal configurations in product lines by random sampling","authors":"Jeho Oh, D. Batory, Margaret Myers, Norbert Siegmund","doi":"10.1145/3106237.3106273","DOIUrl":"https://doi.org/10.1145/3106237.3106273","url":null,"abstract":"Software Product Lines (SPLs) are highly configurable systems. This raises the challenge to find optimal performing configurations for an anticipated workload. As SPL configuration spaces are huge, it is infeasible to benchmark all configurations to find an optimal one. Prior work focused on building performance models to predict and optimize SPL configurations. Instead, we randomly sample and recursively search a configuration space directly to find near-optimal configurations without constructing a prediction model. Our algorithms are simpler and have higher accuracy and efficiency.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131445672","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}
During software development, developers communicate a lot and with many different people. Communication is an important factor, to the point that communication failures are seen as the causes of productivity losses or even project failures. To communicate with each other, software developers use many different tools like mailing lists, forums, issue trackers or chats. Even in a short time span, a lot of information artifacts can arise through these channels, which can be very time consuming to get through after a long vacation or for new members of the team. This paper describes a research plan for an approach which can summarize different communication sources into one big summary using and improving existing text summarization approaches. The resulting tool would have the potential to decrease the effort needed for sense-making and comprehension of communication, as well as the time needed for locating and using information from the communication sources. This reduction in effort will result in a significant increase in the productivity of software development companies.
{"title":"Summarizing software engineering communication artifacts from different sources","authors":"Verena Käfer","doi":"10.1145/3106237.3119877","DOIUrl":"https://doi.org/10.1145/3106237.3119877","url":null,"abstract":"During software development, developers communicate a lot and with many different people. Communication is an important factor, to the point that communication failures are seen as the causes of productivity losses or even project failures. To communicate with each other, software developers use many different tools like mailing lists, forums, issue trackers or chats. Even in a short time span, a lot of information artifacts can arise through these channels, which can be very time consuming to get through after a long vacation or for new members of the team. This paper describes a research plan for an approach which can summarize different communication sources into one big summary using and improving existing text summarization approaches. The resulting tool would have the potential to decrease the effort needed for sense-making and comprehension of communication, as well as the time needed for locating and using information from the communication sources. This reduction in effort will result in a significant increase in the productivity of software development companies.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115914174","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}
Models are key artifacts in model driven software engineering, similar to source code in traditional software engineering. Integrated development environments help users while writing source code, e.g. with typed auto completions, quick fixes, or automatic refactorings. Similar integrated features are rare for modeling IDEs. The above source code IDE features can be seen as a recommender system. A recommender system for model driven software engineering can combine data from different sources in order to infer a list of relevant and actionable model changes in real time. These recommendations can speed up working on models by automating repetitive tasks and preventing errors when the changes are atypical for the changed models. Recommendations can be based on common model transformations that are taken from the literature or learned from models in version control systems. Further information can be taken from instance- to meta-model relationships, modeling related artifacts (e.g. correctness constraints), and versions histories of models under version control. We created a prototype recommender that analyses the change history of a single model. We computed its accuracy via cross-validation and found that it was between 0.43 and 0.82 for models from an open source project. In order to have a bigger data set for the evaluation and the learning of model transformation, we also mined repositories from Eclipse projects for Ecore meta models and their versions. We found 4374 meta models with 17249 versions. 244 of these meta models were changed at least ten times and are candidates for learning common model transformations. We plan to evaluate our recommender system in two ways: (1) In off-line evaluations with data sets of models from the literature, created by us, or taken from industry partners. (2) In on-line user studies with participants from academia and industry, performed as case studies and controlled experiments.
{"title":"Recommender system for model driven software development","authors":"Stefan Kögel","doi":"10.1145/3106237.3119874","DOIUrl":"https://doi.org/10.1145/3106237.3119874","url":null,"abstract":"Models are key artifacts in model driven software engineering, similar to source code in traditional software engineering. Integrated development environments help users while writing source code, e.g. with typed auto completions, quick fixes, or automatic refactorings. Similar integrated features are rare for modeling IDEs. The above source code IDE features can be seen as a recommender system. A recommender system for model driven software engineering can combine data from different sources in order to infer a list of relevant and actionable model changes in real time. These recommendations can speed up working on models by automating repetitive tasks and preventing errors when the changes are atypical for the changed models. Recommendations can be based on common model transformations that are taken from the literature or learned from models in version control systems. Further information can be taken from instance- to meta-model relationships, modeling related artifacts (e.g. correctness constraints), and versions histories of models under version control. We created a prototype recommender that analyses the change history of a single model. We computed its accuracy via cross-validation and found that it was between 0.43 and 0.82 for models from an open source project. In order to have a bigger data set for the evaluation and the learning of model transformation, we also mined repositories from Eclipse projects for Ecore meta models and their versions. We found 4374 meta models with 17249 versions. 244 of these meta models were changed at least ten times and are candidates for learning common model transformations. We plan to evaluate our recommender system in two ways: (1) In off-line evaluations with data sets of models from the literature, created by us, or taken from industry partners. (2) In on-line user studies with participants from academia and industry, performed as case studies and controlled experiments.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122476287","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}
Lint is a widely-used static analyzer for detecting bugs/issues in Android apps. However, it can generate many false warnings. One existing solution to this problem is to leverage project history data (e.g., bug fixing statistics) for warning prioritization. Unfortunately, such techniques are biased toward a project’s archived warnings and can easily miss newissues. Anotherweakness is that developers cannot readily relate the warnings to the impacts perceivable by users. To overcome these weaknesses, in this paper, we propose a semantics-aware approach, OASIS, to prioritizing Lint warnings by leveraging app user reviews. OASIS combines program analysis and NLP techniques to recover the intrinsic links between the Lint warnings for a given app and the user complaints on the app problems caused by the issues of concern. OASIS leverages the strength of such links to prioritize warnings. We evaluated OASIS on six popular and large-scale open-source Android apps. The results show that OASIS can effectively prioritize Lint warnings and help identify new issues that are previously-unknown to app developers.
{"title":"OASIS: prioritizing static analysis warnings for Android apps based on app user reviews","authors":"Lili Wei, Yepang Liu, S. Cheung","doi":"10.1145/3106237.3106294","DOIUrl":"https://doi.org/10.1145/3106237.3106294","url":null,"abstract":"Lint is a widely-used static analyzer for detecting bugs/issues in Android apps. However, it can generate many false warnings. One existing solution to this problem is to leverage project history data (e.g., bug fixing statistics) for warning prioritization. Unfortunately, such techniques are biased toward a project’s archived warnings and can easily miss newissues. Anotherweakness is that developers cannot readily relate the warnings to the impacts perceivable by users. To overcome these weaknesses, in this paper, we propose a semantics-aware approach, OASIS, to prioritizing Lint warnings by leveraging app user reviews. OASIS combines program analysis and NLP techniques to recover the intrinsic links between the Lint warnings for a given app and the user complaints on the app problems caused by the issues of concern. OASIS leverages the strength of such links to prioritize warnings. We evaluated OASIS on six popular and large-scale open-source Android apps. The results show that OASIS can effectively prioritize Lint warnings and help identify new issues that are previously-unknown to app developers.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123225657","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}
Yuekang Li, Bihuan Chen, Mahinthan Chandramohan, Shang-Wei Lin, Yang Liu, Alwen Tiu
Coverage-based fuzzing is one of the most effective techniques to find vulnerabilities, bugs or crashes. However, existing techniques suffer from the difficulty in exercising the paths that are protected by magic bytes comparisons (e.g., string equality comparisons). Several approaches have been proposed to use heavy-weight program analysis to break through magic bytes comparisons, and hence are less scalable. In this paper, we propose a program-state based binary fuzzing approach, named Steelix, which improves the penetration power of a fuzzer at the cost of an acceptable slow down of the execution speed. In particular, we use light-weight static analysis and binary instrumentation to provide not only coverage information but also comparison progress information to a fuzzer. Such program state information informs a fuzzer about where the magic bytes are located in the test input and how to perform mutations to match the magic bytes efficiently. We have implemented Steelix and evaluated it on three datasets: LAVA-M dataset, DARPA CGC sample binaries and five real-life programs. The results show that Steelix has better code coverage and bug detection capability than the state-of-the-art fuzzers. Moreover, we found one CVE and nine new bugs.
{"title":"Steelix: program-state based binary fuzzing","authors":"Yuekang Li, Bihuan Chen, Mahinthan Chandramohan, Shang-Wei Lin, Yang Liu, Alwen Tiu","doi":"10.1145/3106237.3106295","DOIUrl":"https://doi.org/10.1145/3106237.3106295","url":null,"abstract":"Coverage-based fuzzing is one of the most effective techniques to find vulnerabilities, bugs or crashes. However, existing techniques suffer from the difficulty in exercising the paths that are protected by magic bytes comparisons (e.g., string equality comparisons). Several approaches have been proposed to use heavy-weight program analysis to break through magic bytes comparisons, and hence are less scalable. In this paper, we propose a program-state based binary fuzzing approach, named Steelix, which improves the penetration power of a fuzzer at the cost of an acceptable slow down of the execution speed. In particular, we use light-weight static analysis and binary instrumentation to provide not only coverage information but also comparison progress information to a fuzzer. Such program state information informs a fuzzer about where the magic bytes are located in the test input and how to perform mutations to match the magic bytes efficiently. We have implemented Steelix and evaluated it on three datasets: LAVA-M dataset, DARPA CGC sample binaries and five real-life programs. The results show that Steelix has better code coverage and bug detection capability than the state-of-the-art fuzzers. Moreover, we found one CVE and nine new bugs.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122919880","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}
The changes confronting contemporary Self-Adaptive Systems (SASs) are characterized by uncertainties in their relationships, priorities, and contexts. To generate adaptation strategies for handling these changes, existing adaptation planning methods, which ignore these uncertainties, must be improved. This thesis explores the possibilities of using Search-Based Software Engineering (SBSE) to establish a search-based planning method capable of handling multiple changes in an uncertain context without defining their priorities. Meanwhile, both the assurance approach to improving the efficiency of adaptation planning and the selection approach to choosing a unique strategy are proposed to solve emerging research questions that arise when such planning method is applied in actual SASs. From this experience, we are able to derive innovative methods for the designers of SASs as a reference, which may observably improve the ability of SASs and promote the widespread use of SBSE in SASs.
{"title":"Using search-based software engineering to handle the changes with uncertainties for self-adaptive systems","authors":"Lu Wang","doi":"10.1145/3106237.3119871","DOIUrl":"https://doi.org/10.1145/3106237.3119871","url":null,"abstract":"The changes confronting contemporary Self-Adaptive Systems (SASs) are characterized by uncertainties in their relationships, priorities, and contexts. To generate adaptation strategies for handling these changes, existing adaptation planning methods, which ignore these uncertainties, must be improved. This thesis explores the possibilities of using Search-Based Software Engineering (SBSE) to establish a search-based planning method capable of handling multiple changes in an uncertain context without defining their priorities. Meanwhile, both the assurance approach to improving the efficiency of adaptation planning and the selection approach to choosing a unique strategy are proposed to solve emerging research questions that arise when such planning method is applied in actual SASs. From this experience, we are able to derive innovative methods for the designers of SASs as a reference, which may observably improve the ability of SASs and promote the widespread use of SBSE in SASs.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126019847","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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