2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)最新文献_第6页

Mining Software Defects: Should We Consider Affected Releases? 挖掘软件缺陷:我们应该考虑受影响的版本吗?

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00075

S. Yatish, Jirayus Jiarpakdee, Patanamon Thongtanunam, C. Tantithamthavorn

{"title":"Mining Software Defects: Should We Consider Affected Releases?","authors":"S. Yatish, Jirayus Jiarpakdee, Patanamon Thongtanunam, C. Tantithamthavorn","doi":"10.1109/ICSE.2019.00075","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00075","url":null,"abstract":"With the rise of the Mining Software Repositories (MSR) field, defect datasets extracted from software repositories play a foundational role in many empirical studies related to software quality. At the core of defect data preparation is the identification of post-release defects. Prior studies leverage many heuristics (e.g., keywords and issue IDs) to identify post-release defects. However, such the heuristic approach is based on several assumptions, which pose common threats to the validity of many studies. In this paper, we set out to investigate the nature of the difference of defect datasets generated by the heuristic approach and the realistic approach that leverages the earliest affected release that is realistically estimated by a software development team for a given defect. In addition, we investigate the impact of defect identification approaches on the predictive accuracy and the ranking of defective modules that are produced by defect models. Through a case study of defect datasets of 32 releases, we find that that the heuristic approach has a large impact on both defect count datasets and binary defect datasets. Surprisingly, we find that the heuristic approach has a minimal impact on defect count models, suggesting that future work should not be too concerned about defect count models that are constructed using heuristic defect datasets. On the other hand, using defect datasets generated by the realistic approach lead to an improvement in the predictive accuracy of defect classification models.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"46 1","pages":"654-665"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77259891","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}

引用次数: 70

When Code Completion Fails: A Case Study on Real-World Completions 当代码完成失败:一个关于真实世界完成的案例研究

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00101

V. Hellendoorn, Sebastian Proksch, H. Gall, Alberto Bacchelli

{"title":"When Code Completion Fails: A Case Study on Real-World Completions","authors":"V. Hellendoorn, Sebastian Proksch, H. Gall, Alberto Bacchelli","doi":"10.1109/ICSE.2019.00101","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00101","url":null,"abstract":"Code completion is commonly used by software developers and is integrated into all major IDE's. Good completion tools can not only save time and effort but may also help avoid incorrect API usage. Many proposed completion tools have shown promising results on synthetic benchmarks, but these benchmarks make no claims about the realism of the completions they test. This lack of grounding in real-world data could hinder our scientific understanding of developer needs and of the efficacy of completion models. This paper presents a case study on 15,000 code completions that were applied by 66 real developers, which we study and contrast with artificial completions to inform future research and tools in this area. We find that synthetic benchmarks misrepresent many aspects of real-world completions; tested completion tools were far less accurate on real-world data. Worse, on the few completions that consumed most of the developers' time, prediction accuracy was less than 20% -- an effect that is invisible in synthetic benchmarks. Our findings have ramifications for future benchmarks, tool design and real-world efficacy: Benchmarks must account for completions that developers use most, such as intra-project APIs; models should be designed to be amenable to intra-project data; and real-world developer trials are essential to quantifying performance on the least predictable completions, which are both most time-consuming and far more typical than artificial data suggests. We publicly release our preprint [https://doi.org/10.5281/zenodo.2565673] and replication data and materials [https://doi.org/10.5281/zenodo.2562249].","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"8 1","pages":"960-970"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89302549","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}

引用次数: 55

Hunting for Bugs in Code Coverage Tools via Randomized Differential Testing 通过随机差异测试在代码覆盖工具中寻找bug

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00061

Yibiao Yang, Yuming Zhou, Hao Sun, Z. Su, Zhiqiang Zuo, Lei Xu, Baowen Xu

{"title":"Hunting for Bugs in Code Coverage Tools via Randomized Differential Testing","authors":"Yibiao Yang, Yuming Zhou, Hao Sun, Z. Su, Zhiqiang Zuo, Lei Xu, Baowen Xu","doi":"10.1109/ICSE.2019.00061","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00061","url":null,"abstract":"Reliable code coverage tools are critically important as it is heavily used to facilitate many quality assurance activities, such as software testing, fuzzing, and debugging. However, little attention has been devoted to assessing the reliability of code coverage tools. In this study, we propose a randomized differential testing approach to hunting for bugs in the most widely used C code coverage tools. Specifically, by generating random input programs, our approach seeks for inconsistencies in code coverage reports produced by different code coverage tools, and then identifies inconsistencies as potential code coverage bugs. To effectively report code coverage bugs, we addressed three specific challenges: (1) How to filter out duplicate test programs as many of them triggering the same bugs in code coverage tools; (2) how to automatically reduce large test programs to much smaller ones that have the same properties; and (3) how to determine which code coverage tools have bugs? The extensive evaluations validate the effectiveness of our approach, resulting in 42 and 28 confirmed/fixed bugs for gcov and llvm-cov, respectively. This case study indicates that code coverage tools are not as reliable as it might have been envisaged. It not only demonstrates the effectiveness of our approach, but also highlights the need to continue improving the reliability of code coverage tools. This work opens up a new direction in code coverage validation which calls for more attention in this area.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"8 1","pages":"488-499"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86713090","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}

引用次数: 18

Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams Java 8流智能并行化的安全自动重构

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00072

Raffi Khatchadourian, Yiming Tang, M. Bagherzadeh, Syed Ahmed

{"title":"Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams","authors":"Raffi Khatchadourian, Yiming Tang, M. Bagherzadeh, Syed Ahmed","doi":"10.1109/ICSE.2019.00072","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00072","url":null,"abstract":"Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ?642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"7 1","pages":"619-630"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81946310","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}

引用次数: 35

Practical GUI Testing of Android Applications Via Model Abstraction and Refinement 通过模型抽象和细化的Android应用程序的实际GUI测试

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00042

Tianxiao Gu, Chengnian Sun, Xiaoxing Ma, Chun Cao, Chang Xu, Yuan Yao, Qirun Zhang, Jian Lu, Z. Su

{"title":"Practical GUI Testing of Android Applications Via Model Abstraction and Refinement","authors":"Tianxiao Gu, Chengnian Sun, Xiaoxing Ma, Chun Cao, Chang Xu, Yuan Yao, Qirun Zhang, Jian Lu, Z. Su","doi":"10.1109/ICSE.2019.00042","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00042","url":null,"abstract":"This paper introduces a new, fully automated modelbased approach for effective testing of Android apps. Different from existing model-based approaches that guide testing with a static GUI model (i.e., the model does not evolve its abstraction during testing, and is thus often imprecise), our approach dynamically optimizes the model by leveraging the runtime information during testing. This capability of model evolution significantly improves model precision, and thus dramatically enhances the testing effectiveness compared to existing approaches, which our evaluation confirms.We have realized our technique in a practical tool, APE. On 15 large, widely-used apps from the Google Play Store, APE outperforms the state-of-the-art Android GUI testing tools in terms of both testing coverage and the number of detected unique crashes. To further demonstrate APE’s effectiveness and usability, we conduct another evaluation of APE on 1,316 popular apps, where it found 537 unique crashes. Out of the 38 reported crashes, 13 have been fixed and 5 have been confirmed.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"5 1","pages":"269-280"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72645971","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}

引用次数: 93

AutoTap: Synthesizing and Repairing Trigger-Action Programs Using LTL Properties AutoTap:使用LTL属性合成和修复触发操作程序

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00043

Lefan Zhang, Weijia He, Jesse Martinez, Noah Brackenbury, Shan Lu, Blase Ur

引用次数: 48

How Practitioners Perceive Coding Proficiency 从业人员如何看待编码熟练程度

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00098

Xin Xia, Zhiyuan Wan, Pavneet Singh Kochhar, D. Lo

引用次数: 18

ActionNet: Vision-Based Workflow Action Recognition From Programming Screencasts ActionNet:基于视觉的工作流动作识别

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00049

Dehai Zhao, Zhenchang Xing, Chunyang Chen, Xin Xia, Guoqiang Li

{"title":"ActionNet: Vision-Based Workflow Action Recognition From Programming Screencasts","authors":"Dehai Zhao, Zhenchang Xing, Chunyang Chen, Xin Xia, Guoqiang Li","doi":"10.1109/ICSE.2019.00049","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00049","url":null,"abstract":"Programming screencasts have two important applications in software engineering context: study developer behaviors, information needs and disseminate software engineering knowledge. Although programming screencasts are easy to produce, they are not easy to analyze or index due to the image nature of the data. Existing techniques extract only content from screencasts, but ignore workflow actions by which developers accomplish programming tasks. This significantly limits the effective use of programming screencasts in downstream applications. In this paper, we are the first to present a novel technique for recognizing workflow actions in programming screencasts. Our technique exploits image differencing and Convolutional Neural Network (CNN) to analyze the correspondence and change of consecutive frames, based on which nine classes of frequent developer actions can be recognized from programming screencasts. Using programming screencasts from Youtube, we evaluate different configurations of our CNN model and the performance of our technique for developer action recognition across developers, working environments and programming languages. Using screencasts of developers’ real work, we demonstrate the usefulness of our technique in a practical application for actionaware extraction of key-code frames in developers’ work.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"77 1","pages":"350-361"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83993841","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}

引用次数: 39

Detection and Repair of Architectural Inconsistencies in Java Java中体系结构不一致的检测和修复

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00067

Negar Ghorbani, Joshua Garcia, S. Malek

{"title":"Detection and Repair of Architectural Inconsistencies in Java","authors":"Negar Ghorbani, Joshua Garcia, S. Malek","doi":"10.1109/ICSE.2019.00067","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00067","url":null,"abstract":"Java is one of the most widely used programming languages. However, the absence of explicit support for architectural constructs, such as software components, in the programming language itself has prevented software developers from achieving the many benefits that come with architecture-based development. To address this issue, Java 9 has introduced the Java Platform Module System (JPMS), resulting in the first instance of encapsulation of modules with rich software architectural interfaces added to a mainstream programming language. The primary goal of JPMS is to construct and maintain large applications efficiently-as well as improve the encapsulation, security, and maintainability of Java applications in general and the JDK itself. A challenge, however, is that module declarations do not necessarily reflect actual usage of modules in an application, allowing developers to mistakenly specify inconsistent dependencies among the modules. In this paper, we formally define 8 inconsistent modular dependencies that may arise in Java-9 applications. We also present DARCY, an approach that leverages these definitions and static program analyses to automatically (1) detect the specified inconsistent dependencies within Java applications and (2) repair those identified inconsistencies. The results of our experiments, conducted over 38 open-source Java-9 applications, indicate that architectural inconsistencies are widespread and demonstrate the benefits of DARCY in automated detection and repair of these inconsistencies.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"10 1","pages":"560-571"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83004484","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}

引用次数: 11

Distance-Based Sampling of Software Configuration Spaces 基于距离的软件组态空间采样

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) Pub Date : 2019-05-01 DOI: 10.1109/ICSE.2019.00112

Christian Kaltenecker, A. Grebhahn, Norbert Siegmund, Jianmei Guo, S. Apel

{"title":"Distance-Based Sampling of Software Configuration Spaces","authors":"Christian Kaltenecker, A. Grebhahn, Norbert Siegmund, Jianmei Guo, S. Apel","doi":"10.1109/ICSE.2019.00112","DOIUrl":"https://doi.org/10.1109/ICSE.2019.00112","url":null,"abstract":"Configurable software systems provide a multitude of configuration options to adjust and optimize their functional and non-functional properties. For instance, to find the fastest configuration for a given setting, a brute-force strategy measures the performance of all configurations, which is typically intractable. Addressing this challenge, state-of-the-art strategies rely on machine learning, analyzing only a few configurations (i.e., a sample set) to predict the performance of other configurations. However, to obtain accurate performance predictions, a representative sample set of configurations is required. Addressing this task, different sampling strategies have been proposed, which come with different advantages (e.g., covering the configuration space systematically) and disadvantages (e.g., the need to enumerate all configurations). In our experiments, we found that most sampling strategies do not achieve a good coverage of the configuration space with respect to covering relevant performance values. That is, they miss important configurations with distinct performance behavior. Based on this observation, we devise a new sampling strategy, called distance-based sampling, that is based on a distance metric and a probability distribution to spread the configurations of the sample set according to a given probability distribution across the configuration space. This way, we cover different kinds of interactions among configuration options in the sample set. To demonstrate the merits of distance-based sampling, we compare it to state-of-the-art sampling strategies, such as t-wise sampling, on $10$ real-world configurable software systems. Our results show that distance-based sampling leads to more accurate performance models for medium to large sample sets.","PeriodicalId":6736,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)","volume":"22 1","pages":"1084-1094"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76515171","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}

引用次数: 81