In Web applications, atomicity violations caused by AJAX (Asynchronous JavaScript and XML) generate non-determinism and inconsistency. This paper introduces a dynamical detecting approach for atomicity violations in AJAX. Implemented based on Jalangi, an existing instrumentation framework, our technique monitors the execution of a Web application and tracks the Function Callback Flow to find atomic regions by taint analysis. Next, we build a dynamic event model, so as to precisely record the related DOM elements in call and callback steps with low overhead. Then we develop an event-based algorithm to acquire the results, which are classified into different severity levels (benign or harmful). Finally, we conduct an empirical experiment on a subset of Alexa top-ranked websites. Our tool detects 175 DOM related atomicity violations in which 48.6% of violations are identified as harmful after manual inspection.
{"title":"Dynamically Detecting DOM-Related Atomicity Violations in JavaScript with Asynchronous Call","authors":"Dezhi Wang, Lei Xu, Baowen Xu, Weifeng Zhang","doi":"10.1109/SATE.2016.14","DOIUrl":"https://doi.org/10.1109/SATE.2016.14","url":null,"abstract":"In Web applications, atomicity violations caused by AJAX (Asynchronous JavaScript and XML) generate non-determinism and inconsistency. This paper introduces a dynamical detecting approach for atomicity violations in AJAX. Implemented based on Jalangi, an existing instrumentation framework, our technique monitors the execution of a Web application and tracks the Function Callback Flow to find atomic regions by taint analysis. Next, we build a dynamic event model, so as to precisely record the related DOM elements in call and callback steps with low overhead. Then we develop an event-based algorithm to acquire the results, which are classified into different severity levels (benign or harmful). Finally, we conduct an empirical experiment on a subset of Alexa top-ranked websites. Our tool detects 175 DOM related atomicity violations in which 48.6% of violations are identified as harmful after manual inspection.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121257597","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}
Pinpointing sources of runtime faults, especially persistent unforeseen ones, is crucial for minimizing failure impact in the surge of service-based processes in e-commerce times. This paper presents a novel probabilistic reasoning method for diagnosing what caused the failures in service-based processes, while assuming that there is no knowledge of fault types and no formal specification of activities at design time, but activity dependence traces are available in running against test cases. Our probabilistic diagnosis is statistically significant in coping with uncertain failures arising from process executions with unknown input and output values for some activities. Experiments are carried out on various scale orchestrated web services with injected faults, and the results show that our probabilistic diagnosis statistically performs better than earlier dependency-based methods.
{"title":"Diagnosis of Service Failures by Probabilistic Inference with Runtime Activity Dependences","authors":"Rong Chen, Yaqing Liu, X. Ge, Hui Li","doi":"10.1109/SATE.2016.16","DOIUrl":"https://doi.org/10.1109/SATE.2016.16","url":null,"abstract":"Pinpointing sources of runtime faults, especially persistent unforeseen ones, is crucial for minimizing failure impact in the surge of service-based processes in e-commerce times. This paper presents a novel probabilistic reasoning method for diagnosing what caused the failures in service-based processes, while assuming that there is no knowledge of fault types and no formal specification of activities at design time, but activity dependence traces are available in running against test cases. Our probabilistic diagnosis is statistically significant in coping with uncertain failures arising from process executions with unknown input and output values for some activities. Experiments are carried out on various scale orchestrated web services with injected faults, and the results show that our probabilistic diagnosis statistically performs better than earlier dependency-based methods.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130146294","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}
Recently, representative sparse representation based classifiers, namely dictionary learning and collaborative representation based classifier (CRC), has been introduced into software defect prediction (SDP) and demonstrated to be effective for SDP. The dictionary learning based SDP method needs relatively large computational cost, while collaborative representation based method can significantly reduce the computational cost and achieve comparable prediction effects as the former. In this paper, we aim to preserve the desirable efficiency of collaborative representation based SDP method and further improve its prediction effect. We propose a cost-sensitive local collaborative representation (CLCR) approach for SDP. CLCR firstly efficiently finds the neighboring modules of a given test (query) module using CRC. Then CLCR represents the test module as a linear combination of its neighbors and uses the representation error for prediction. To solve the class-imbalance problem, CLCR further incorporates the cost-sensitive factor into the representation coefficients in the prediction phase. Experiments on five projects of the NASA dataset demonstrate the effectiveness of the proposed approach as compared with several related SDP methods.
{"title":"Cost-Sensitive Local Collaborative Representation for Software Defect Prediction","authors":"Fei Wu, Xiaoyuan Jing, Xiwei Dong, Jicheng Cao, Baowen Xu, Shi Ying","doi":"10.1109/SATE.2016.24","DOIUrl":"https://doi.org/10.1109/SATE.2016.24","url":null,"abstract":"Recently, representative sparse representation based classifiers, namely dictionary learning and collaborative representation based classifier (CRC), has been introduced into software defect prediction (SDP) and demonstrated to be effective for SDP. The dictionary learning based SDP method needs relatively large computational cost, while collaborative representation based method can significantly reduce the computational cost and achieve comparable prediction effects as the former. In this paper, we aim to preserve the desirable efficiency of collaborative representation based SDP method and further improve its prediction effect. We propose a cost-sensitive local collaborative representation (CLCR) approach for SDP. CLCR firstly efficiently finds the neighboring modules of a given test (query) module using CRC. Then CLCR represents the test module as a linear combination of its neighbors and uses the representation error for prediction. To solve the class-imbalance problem, CLCR further incorporates the cost-sensitive factor into the representation coefficients in the prediction phase. Experiments on five projects of the NASA dataset demonstrate the effectiveness of the proposed approach as compared with several related SDP methods.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131041601","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}
Xiaodong Zhang, Z. Yang, Q. Zheng, Yu Hao, Pei Liu, Lechen Yu, Ming Fan, Ting Liu
Debugging multithreaded software is challenging because the basic assumption that underlies sequential software debugging, i.e. the program behavior is deterministic under fixed inputs, is no longer valid due to the nondeterminism brought by thread scheduling. In this paper, we propose a proactive debugging method to restore this basic assumption so that programmers can debug multithreaded programs as if they were sequential. Our approach is based on the synergistic integration of a set of new symbolic analysis and dynamic analysis techniques. In particular, symbolic analysis is used to investigate the program behavior under multiple thread interleavings and then drive the dynamic execution to new branches. Dynamic analysis is used to execute these new branches and in turn guide the symbolic analysis further. The net effect of applying this feedback loop is a systematic and complete coverage of the program behavior under a fixed test input. We have implemented the proposed method in a software tool called Proactive-Debugger. Our experiments show that Proactive-Debugger outperforms both ESBMC and Maple, two state-of-the-art testing tools for detecting and reproducing bugs in multithreaded programs.
{"title":"Debugging Multithreaded Programs as if They Were Sequential","authors":"Xiaodong Zhang, Z. Yang, Q. Zheng, Yu Hao, Pei Liu, Lechen Yu, Ming Fan, Ting Liu","doi":"10.1109/SATE.2016.20","DOIUrl":"https://doi.org/10.1109/SATE.2016.20","url":null,"abstract":"Debugging multithreaded software is challenging because the basic assumption that underlies sequential software debugging, i.e. the program behavior is deterministic under fixed inputs, is no longer valid due to the nondeterminism brought by thread scheduling. In this paper, we propose a proactive debugging method to restore this basic assumption so that programmers can debug multithreaded programs as if they were sequential. Our approach is based on the synergistic integration of a set of new symbolic analysis and dynamic analysis techniques. In particular, symbolic analysis is used to investigate the program behavior under multiple thread interleavings and then drive the dynamic execution to new branches. Dynamic analysis is used to execute these new branches and in turn guide the symbolic analysis further. The net effect of applying this feedback loop is a systematic and complete coverage of the program behavior under a fixed test input. We have implemented the proposed method in a software tool called Proactive-Debugger. Our experiments show that Proactive-Debugger outperforms both ESBMC and Maple, two state-of-the-art testing tools for detecting and reproducing bugs in multithreaded programs.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129032881","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}
As a traditional dynamic language, Python is increasingly used in various software engineering tasks. However, due to its flexibility and dynamism, Python is a particularly challenging language to write code in and maintain. Consequently, Python programs contain code smells which indicate potential comprehension and maintenance problems. With the aim of supporting refactoring strategies to enhance maintainability, this paper describes how to detect code smells in Python programs. We introduce 11 Python smells and describe the detection strategy. We also implement a smell detection tool named Pysmell and use it to identify code smells in five real world Python systems. The results show that Pysmell can detect 285 code smell instances in total with the average precision of 97.7%. It reveals that Large Class and Large Method are most prevalent. Our experiment also implies Python programs may be suffering code smells further.
{"title":"Detecting Code Smells in Python Programs","authors":"Zhifei Chen, Lin Chen, Wanwangying Ma, Baowen Xu","doi":"10.1109/SATE.2016.10","DOIUrl":"https://doi.org/10.1109/SATE.2016.10","url":null,"abstract":"As a traditional dynamic language, Python is increasingly used in various software engineering tasks. However, due to its flexibility and dynamism, Python is a particularly challenging language to write code in and maintain. Consequently, Python programs contain code smells which indicate potential comprehension and maintenance problems. With the aim of supporting refactoring strategies to enhance maintainability, this paper describes how to detect code smells in Python programs. We introduce 11 Python smells and describe the detection strategy. We also implement a smell detection tool named Pysmell and use it to identify code smells in five real world Python systems. The results show that Pysmell can detect 285 code smell instances in total with the average precision of 97.7%. It reveals that Large Class and Large Method are most prevalent. Our experiment also implies Python programs may be suffering code smells further.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131133166","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}
Testing-based Fault Localization (TBFL) can guide and automate the process of program debugging by providing developers with a ranked list of suspicious statements. However, collecting the testing information of the whole original test-suite is always too expensive or even infeasible for developers to conduct efficient TBFL. Traditional Test-Suite Reduction (TSR) approaches can be utilized to reduce the size of test-suite. But they still rely on the time-consuming process of the whole testing information collection. In this paper, we propose a distance-based test-suite reduction (DTSR) approach. It is guided by the distances between the test cases rather than the whole testing information when conducting the test-suite reduction. Compared with the existing TSRs, DTSR only needs to collect the testing information of a part of test cases. Our investigation on a series of benchmarks shows that DTSR can effectively reduce the size of the given test-suite and the time cost of TBFL. Nerveless, the fault localization effective-ness of our approach is close to that of the whole test-suite.
{"title":"Distance-Based Test-Suite Reduction for Efficient Testing-Based Fault Localization","authors":"Xingya Wang, Shujuan Jiang, Pengfei Gao, Xiaolin Ju, Rongcun Wang, Yanmei Zhang","doi":"10.1109/SATE.2016.21","DOIUrl":"https://doi.org/10.1109/SATE.2016.21","url":null,"abstract":"Testing-based Fault Localization (TBFL) can guide and automate the process of program debugging by providing developers with a ranked list of suspicious statements. However, collecting the testing information of the whole original test-suite is always too expensive or even infeasible for developers to conduct efficient TBFL. Traditional Test-Suite Reduction (TSR) approaches can be utilized to reduce the size of test-suite. But they still rely on the time-consuming process of the whole testing information collection. In this paper, we propose a distance-based test-suite reduction (DTSR) approach. It is guided by the distances between the test cases rather than the whole testing information when conducting the test-suite reduction. Compared with the existing TSRs, DTSR only needs to collect the testing information of a part of test cases. Our investigation on a series of benchmarks shows that DTSR can effectively reduce the size of the given test-suite and the time cost of TBFL. Nerveless, the fault localization effective-ness of our approach is close to that of the whole test-suite.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132144789","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 structures and behaviors of modern software systems are more and more complicated, thus the modeling of the runtime software systems is difficult. In this paper, we model the runtime Linux operating system (LOS) as a weighted network to investigate the execution process of LOS. The topologies of the weighted LOS network are analyzed and it is found that the weight distribution follows a power-law distribution. For better understanding the execution process of LOS, we explore the manifestations of LOS components. The result shows that the component of process management plays the key role in the execution process of LOS. Moreover, an assessment of the reliability of LOS is proposed by considering the execution status of LOS as a discrete time Markov chain. The reliabilities of 10 LOS versions ranging from versions 3.15 to 4.4 are compared and the result shows that the reliability declines. Our work may shed a light on the testing and monitoring processes of software systems.
{"title":"Analysis of the Runtime Linux Operating System as a Complex Weighted Network","authors":"Haoqin Wang, Guanping Xiao","doi":"10.1109/SATE.2016.8","DOIUrl":"https://doi.org/10.1109/SATE.2016.8","url":null,"abstract":"The structures and behaviors of modern software systems are more and more complicated, thus the modeling of the runtime software systems is difficult. In this paper, we model the runtime Linux operating system (LOS) as a weighted network to investigate the execution process of LOS. The topologies of the weighted LOS network are analyzed and it is found that the weight distribution follows a power-law distribution. For better understanding the execution process of LOS, we explore the manifestations of LOS components. The result shows that the component of process management plays the key role in the execution process of LOS. Moreover, an assessment of the reliability of LOS is proposed by considering the execution status of LOS as a discrete time Markov chain. The reliabilities of 10 LOS versions ranging from versions 3.15 to 4.4 are compared and the result shows that the reliability declines. Our work may shed a light on the testing and monitoring processes of software systems.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121928036","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}
Lei Xiao, Huai-kou Miao, Weiwei Zhuang, Shaojun Chen
Cluster application is proposed as an efficient approach to improve test case prioritization, Test case in a same cluster are considered to have similar behaviors. In the process of cluster test case, the selection of test case feature and the clusters number have great influence on the clustering results. but to date almost clustering algorithm to improve test case prioritization are selected random clusters number and clustering result are based on one or a few of the code features, the paper propose a new prioritization techniques that not only consider the best clusters number but also produce the best clustering result based on test case multidimensional feature. After clustering, considering the inter-cluster prioritization and intra-cluster prioritization,in order to improve the effectiveness of our approach, the fault prediction value of code corresponding to the test case is used as one of a prioritization weight. Finally,we implemented an empirical studies using an industrial software to illustrate the effectiveness of the test case prioritization techniques.
{"title":"Applying Assemble Clustering Algorithm and Fault Prediction to Test Case Prioritization","authors":"Lei Xiao, Huai-kou Miao, Weiwei Zhuang, Shaojun Chen","doi":"10.1109/SATE.2016.25","DOIUrl":"https://doi.org/10.1109/SATE.2016.25","url":null,"abstract":"Cluster application is proposed as an efficient approach to improve test case prioritization, Test case in a same cluster are considered to have similar behaviors. In the process of cluster test case, the selection of test case feature and the clusters number have great influence on the clustering results. but to date almost clustering algorithm to improve test case prioritization are selected random clusters number and clustering result are based on one or a few of the code features, the paper propose a new prioritization techniques that not only consider the best clusters number but also produce the best clustering result based on test case multidimensional feature. After clustering, considering the inter-cluster prioritization and intra-cluster prioritization,in order to improve the effectiveness of our approach, the fault prediction value of code corresponding to the test case is used as one of a prioritization weight. Finally,we implemented an empirical studies using an industrial software to illustrate the effectiveness of the test case prioritization techniques.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129086644","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}
Yihao Li, Dong Li, Fuqun Huang, Shou-Yu Lee, J. Ai
Understanding how software developers' erroneous tendency changes across time has significant implications for building fault-proneness prediction models and guiding software evolution testing. This paper initiates the investigation on software developers' bug-introducing tendency through an exploratory analysis. Five metrics are proposed to capture software developers' bug-introducing tendency and its correlated factors. A total of 76 software developers, working in four widely used software programs from GitHub, have been analyzed. The initial findings are presented.
{"title":"An Exploratory Analysis on Software Developers' Bug-Introducing Tendency over Time","authors":"Yihao Li, Dong Li, Fuqun Huang, Shou-Yu Lee, J. Ai","doi":"10.1109/SATE.2016.9","DOIUrl":"https://doi.org/10.1109/SATE.2016.9","url":null,"abstract":"Understanding how software developers' erroneous tendency changes across time has significant implications for building fault-proneness prediction models and guiding software evolution testing. This paper initiates the investigation on software developers' bug-introducing tendency through an exploratory analysis. Five metrics are proposed to capture software developers' bug-introducing tendency and its correlated factors. A total of 76 software developers, working in four widely used software programs from GitHub, have been analyzed. The initial findings are presented.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335580","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}
Lightweight fault localization technique is a popular class of automated approach to assist programmers in debugging, which often outputs an ordered list of program entities sorted based on their likelihood to be the root cause of a set of failures. However, the technique only focus on calculating the association between program entity and failures, and did not consider program entity's fault context may influence the result. A lightweight fault localization based on fault-context was proposed, which combine suspiciousness of program entity and suspiciousness of program entity's fault-context. We conducted an experiment in which our approach was applied to seven benchmark programs. The experimental results show that our approach combining DStar and fault-context can improve absolute ranking with effective rate of 34.8% for 132 faulty versions from the seven benchmark programs. It is worth mentioning that our approach can obtain average improvement of 65.18% for those improved program if those is effective to SFL, and root causes of failures of 7 buggy programs were improved ranking at the top in the fault ranking report.
{"title":"Lightweight Fault Localization Combining with Fault-Context","authors":"Yong Wang, Zhiqiu Huang, Yong Li, Bingwu Fang","doi":"10.1109/SATE.2016.23","DOIUrl":"https://doi.org/10.1109/SATE.2016.23","url":null,"abstract":"Lightweight fault localization technique is a popular class of automated approach to assist programmers in debugging, which often outputs an ordered list of program entities sorted based on their likelihood to be the root cause of a set of failures. However, the technique only focus on calculating the association between program entity and failures, and did not consider program entity's fault context may influence the result. A lightweight fault localization based on fault-context was proposed, which combine suspiciousness of program entity and suspiciousness of program entity's fault-context. We conducted an experiment in which our approach was applied to seven benchmark programs. The experimental results show that our approach combining DStar and fault-context can improve absolute ranking with effective rate of 34.8% for 132 faulty versions from the seven benchmark programs. It is worth mentioning that our approach can obtain average improvement of 65.18% for those improved program if those is effective to SFL, and root causes of failures of 7 buggy programs were improved ranking at the top in the fault ranking report.","PeriodicalId":344531,"journal":{"name":"2016 International Conference on Software Analysis, Testing and Evolution (SATE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131016929","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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