2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)最新文献_第5页

IdBench: Evaluating Semantic Representations of Identifier Names in Source Code IdBench:评估源代码中标识符名称的语义表示

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-05-01 DOI: 10.1109/ICSE43902.2021.00059

Yaza Wainakh, Moiz Rauf, Michael Pradel

{"title":"IdBench: Evaluating Semantic Representations of Identifier Names in Source Code","authors":"Yaza Wainakh, Moiz Rauf, Michael Pradel","doi":"10.1109/ICSE43902.2021.00059","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00059","url":null,"abstract":"Identifier names convey useful information about the intended semantics of code. Name-based program analyses use this information, e.g., to detect bugs, to predict types, and to improve the readability of code. At the core of name-based analyses are semantic representations of identifiers, e.g., in the form of learned embeddings. The high-level goal of such a representation is to encode whether two identifiers, e.g., len and size, are semantically similar. Unfortunately, it is currently unclear to what extent semantic representations match the semantic relatedness and similarity perceived by developers. This paper presents IdBench, the first benchmark for evaluating semantic representations against a ground truth created from thousands of ratings by 500 software developers. We use IdBench to study state-of-the-art embedding techniques proposed for natural language, an embedding technique specifically designed for source code, and lexical string distance functions. Our results show that the effectiveness of semantic representations varies significantly and that the best available embeddings successfully represent semantic relatedness. On the downside, no existing technique provides a satisfactory representation of semantic similarities, among other reasons because identifiers with opposing meanings are incorrectly considered to be similar, which may lead to fatal mistakes, e.g., in a refactoring tool. Studying the strengths and weaknesses of the different techniques shows that they complement each other. As a first step toward exploiting this complementarity, we present an ensemble model that combines existing techniques and that clearly outperforms the best available semantic representation.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129869044","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: 21

Onboarding vs. Diversity, Productivity and Quality — Empirical Study of the OpenStack Ecosystem 入职vs多样性，生产力和质量——OpenStack生态系统的实证研究

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-05-01 DOI: 10.1109/ICSE43902.2021.00097

A. Foundjem, Ellis E. Eghan, Bram Adams

{"title":"Onboarding vs. Diversity, Productivity and Quality — Empirical Study of the OpenStack Ecosystem","authors":"A. Foundjem, Ellis E. Eghan, Bram Adams","doi":"10.1109/ICSE43902.2021.00097","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00097","url":null,"abstract":"Despite the growing success of open-source software ecosystems (SECOs), their sustainability depends on the recruitment and involvement of ever-larger contributors. As such, onboarding, i.e., the socio-technical adaptation of new contributors to a SECO, forms a significant aspect of a SECO's growth that requires substantial resources. Unfortunately, despite theoretical models and initial user studies to examine the potential benefits of onboarding, little is known about the process of SECO onboarding, nor about the socio-technical benefits and drawbacks of contributors' onboarding experience in a SECO. To address these, we first carry out an observational study of 72 new contributors during an OpenStack onboarding event to provide a catalog of teaching content, teaching strategies, onboarding challenges, and expected benefits. Next, we empirically validate the extent to which diversity, productivity, and quality benefits are achieved by mining code changes, reviews, and contributors' issues with(out) OpenStack onboarding experience. Among other findings, our study shows a significant correlation with increasing gender diversity (65% for both females and non-binary contributors) and patch acceptance rates (13.5%). Onboarding also has a significant negative correlation with the time until a contributor's first commit and bug-proneness of contributions.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114372302","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: 8

FlakeFlagger: Predicting Flakiness Without Rerunning Tests FlakeFlagger:在不重新运行测试的情况下预测异常

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-05-01 DOI: 10.1109/ICSE43902.2021.00140

A. Alshammari, Christopher Morris, Michael C Hilton, Jonathan Bell

{"title":"FlakeFlagger: Predicting Flakiness Without Rerunning Tests","authors":"A. Alshammari, Christopher Morris, Michael C Hilton, Jonathan Bell","doi":"10.1109/ICSE43902.2021.00140","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00140","url":null,"abstract":"When developers make changes to their code, they typically run regression tests to detect if their recent changes (re) introduce any bugs. However, many tests are flaky, and their outcomes can change non-deterministically, failing without apparent cause. Flaky tests are a significant nuisance in the development process, since they make it more difficult for developers to trust the outcome of their tests, and hence, it is important to know which tests are flaky. The traditional approach to identify flaky tests is to rerun them multiple times: if a test is observed both passing and failing on the same code, it is definitely flaky. We conducted a very large empirical study looking for flaky tests by rerunning the test suites of 24 projects 10,000 times each, and found that even with this many reruns, some previously identified flaky tests were still not detected. We propose FlakeFlagger, a novel approach that collects a set of features describing the behavior of each test, and then predicts tests that are likely to be flaky based on similar behavioral features. We found that FlakeFlagger correctly labeled as flaky at least as many tests as a state-of-the-art flaky test classifier, but that FlakeFlagger reported far fewer false positives. This lower false positive rate translates directly to saved time for researchers and developers who use the classification result to guide more expensive flaky test detection processes. Evaluated on our dataset of 23 projects with flaky tests, FlakeFlagger outperformed the prior approach (by F1 score) on 16 projects and tied on 4 projects. Our results indicate that this approach can be effective for identifying likely flaky tests prior to running time-consuming flaky test detectors.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114502597","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: 51

DeepLV: Suggesting Log Levels Using Ordinal Based Neural Networks DeepLV:使用基于顺序的神经网络建议日志级别

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-05-01 DOI: 10.1109/ICSE43902.2021.00131

Zhenhao Li, Heng Li, T. Chen, Weiyi Shang

{"title":"DeepLV: Suggesting Log Levels Using Ordinal Based Neural Networks","authors":"Zhenhao Li, Heng Li, T. Chen, Weiyi Shang","doi":"10.1109/ICSE43902.2021.00131","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00131","url":null,"abstract":"Developers write logging statements to generate logs that provide valuable runtime information for debugging and maintenance of software systems. Log level is an important component of a logging statement, which enables developers to control the information to be generated at system runtime. However, due to the complexity of software systems and their runtime behaviors, deciding a proper log level for a logging statement is a challenging task. For example, choosing a higher level (e.g., error) for a trivial event may confuse end users and increase system maintenance overhead, while choosing a lower level (e.g., trace) for a critical event may prevent the important execution information to be conveyed opportunely. In this paper, we tackle the challenge by first conducting a preliminary manual study on the characteristics of log levels. We find that the syntactic context of the logging statement and the message to be logged might be related to the decision of log levels, and log levels that are further apart in order (e.g., trace and error) tend to have more differences in their characteristics. Based on this, we then propose a deep-learning based approach that can leverage the ordinal nature of log levels to make suggestions on choosing log levels, by using the syntactic context and message features of the logging statements extracted from the source code. Through an evaluation on nine large-scale open source projects, we find that: 1) our approach outperforms the state-of-the-art baseline approaches; 2) we can further improve the performance of our approach by enlarging the training data obtained from other systems; 3) our approach also achieves promising results on cross-system suggestions that are even better than the baseline approaches on within-system suggestions. Our study highlights the potentials in suggesting log levels to help developers make informed logging decisions.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"314 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122316144","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: 30

Automatic Extraction of Opinion-Based Q&A from Online Developer Chats 从在线开发人员聊天中自动提取基于意见的问答

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-05-01 DOI: 10.1109/ICSE43902.2021.00115

Preetha Chatterjee, Kostadin Damevski, L. Pollock

{"title":"Automatic Extraction of Opinion-Based Q&A from Online Developer Chats","authors":"Preetha Chatterjee, Kostadin Damevski, L. Pollock","doi":"10.1109/ICSE43902.2021.00115","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00115","url":null,"abstract":"Virtual conversational assistants designed specifically for software engineers could have a huge impact on the time it takes for software engineers to get help. Research efforts are focusing on virtual assistants that support specific software development tasks such as bug repair and pair programming. In this paper, we study the use of online chat platforms as a resource towards collecting developer opinions that could potentially help in building opinion Q&A systems, as a specialized instance of virtual assistants and chatbots for software engineers. Opinion Q&A has a stronger presence in chats than in other developer communications, thus mining them can provide a valuable resource for developers in quickly getting insight about a specific development topic (e.g., What is the best Java library for parsing JSON?). We address the problem of opinion Q&A extraction by developing automatic identification of opinion-asking questions and extraction of participants' answers from public online developer chats. We evaluate our automatic approaches on chats spanning six programming communities and two platforms. Our results show that a heuristic approach to opinion-asking questions works well (.87 precision), and a deep learning approach customized to the software domain outperforms heuristics-based, machine-learning-based and deep learning for answer extraction in community question answering.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132104455","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: 19

Using Domain-Specific Corpora for Improved Handling of Ambiguity in Requirements 使用领域特定语料库改进需求中歧义的处理

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-05-01 DOI: 10.1109/ICSE43902.2021.00133

Saad Ezzini, Sallam Abualhaija, Chetan Arora, M. Sabetzadeh, L. Briand

{"title":"Using Domain-Specific Corpora for Improved Handling of Ambiguity in Requirements","authors":"Saad Ezzini, Sallam Abualhaija, Chetan Arora, M. Sabetzadeh, L. Briand","doi":"10.1109/ICSE43902.2021.00133","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00133","url":null,"abstract":"Ambiguity in natural-language requirements is a pervasive issue that has been studied by the requirements engineering community for more than two decades. A fully manual approach for addressing ambiguity in requirements is tedious and time-consuming, and may further overlook unacknowledged ambiguity – the situation where different stakeholders perceive a requirement as unambiguous but, in reality, interpret the requirement differently. In this paper, we propose an automated approach that uses natural language processing for handling ambiguity in requirements. Our approach is based on the automatic generation of a domain-specific corpus from Wikipedia. Integrating domain knowledge, as we show in our evaluation, leads to a significant positive improvement in the accuracy of ambiguity detection and interpretation. We scope our work to coordination ambiguity (CA) and prepositional-phrase attachment ambiguity (PAA) because of the prevalence of these types of ambiguity in natural-language requirements [1]. We evaluate our approach on 20 industrial requirements documents. These documents collectively contain more than 5000 requirements from seven distinct application domains. Over this dataset, our approach detects CA and PAA with an average precision of 80% and an average recall of 89% (90% for cases of unacknowledged ambiguity). The automatic interpretations that our approach yields have an average accuracy of 85%. Compared to baselines that use generic corpora, our approach, which uses domain-specific corpora, has 33% better accuracy in ambiguity detection and 16% better accuracy in interpretation.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121796469","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: 28

Automatic Solution Summarization for Crash Bugs 崩溃bug的自动解决方案摘要

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-05-01 DOI: 10.1109/ICSE43902.2021.00117

Haoye Wang, Xin Xia, David Lo, J. Grundy, Xinyu Wang

{"title":"Automatic Solution Summarization for Crash Bugs","authors":"Haoye Wang, Xin Xia, David Lo, J. Grundy, Xinyu Wang","doi":"10.1109/ICSE43902.2021.00117","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00117","url":null,"abstract":"The causes of software crashes can be hidden anywhere in the source code and development environment. When encountering software crashes, recurring bugs that are discussed on Q&A sites could provide developers with solutions to their crashing problems. However, it is difficult for developers to accurately search for relevant content on search engines, and developers have to spend a lot of manual effort to find the right solution from the returned results. In this paper, we present CRASOLVER, an approach that takes into account both the structural information of crash traces and the knowledge of crash-causing bugs to automatically summarize solutions from crash traces. Given a crash trace, CRASOLVER retrieves relevant questions from Q&A sites by combining a proposed position dependent similarity – based on the structural information of the crash trace – with an extra knowledge similarity, based on the knowledge from official documentation sites. After obtaining the answers to these questions from the Q&A site, CRASOLVER summarizes the final solution based on a multi-factor scoring mechanism. To evaluate our approach, we built two repositories of Java and Android exception-related questions from Stack Overflow with size of 69,478 and 33,566 questions respectively. Our user study results using 50 selected Java crash traces and 50 selected Android crash traces show that our approach significantly outperforms four baselines in terms of relevance, usefulness, and diversity. The evaluation also confirms the effectiveness of the relevant question retrieval component in our approach for crash traces.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129858222","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: 9

Interpretation-Enabled Software Reuse Detection Based on a Multi-level Birthmark Model 基于多级胎记模型的支持解释的软件重用检测

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-03-18 DOI: 10.1109/ICSE43902.2021.00084

Xi Xu, Q. Zheng, Zheng Yan, Ming Fan, Ang Jia, Ting Liu

{"title":"Interpretation-Enabled Software Reuse Detection Based on a Multi-level Birthmark Model","authors":"Xi Xu, Q. Zheng, Zheng Yan, Ming Fan, Ang Jia, Ting Liu","doi":"10.1109/ICSE43902.2021.00084","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00084","url":null,"abstract":"Software reuse, especially partial reuse, poses legal and security threats to software development. Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand, current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalization to extract core semantics of instructions. For efficiently detecting reused functions, a process for \"intent search based on anchor recognition\" is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94.8% recall. Moreover, it is resilient to cross-compilation, outperforming state-of-the-art approaches.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123847183","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: 12

Does Mutation Testing Improve Testing Practices? 突变检测是否改进了检测实践?

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-03-12 DOI: 10.1109/ICSE43902.2021.00087

Goran R. Petrovi'c, Marko Ivankovi'c, G. Fraser, René Just

{"title":"Does Mutation Testing Improve Testing Practices?","authors":"Goran R. Petrovi'c, Marko Ivankovi'c, G. Fraser, René Just","doi":"10.1109/ICSE43902.2021.00087","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00087","url":null,"abstract":"Various proxy metrics for test quality have been defined in order to guide developers when writing tests. Code coverage is particularly well established in practice, even though the question of how coverage relates to test quality is a matter of ongoing debate. Mutation testing offers a promising alternative: Artificial defects can identify holes in a test suite, and thus provide concrete suggestions for additional tests. Despite the obvious advantages of mutation testing, it is not yet well established in practice. Until recently, mutation testing tools and techniques simply did not scale to complex systems. Although they now do scale, a remaining obstacle is lack of evidence that writing tests for mutants actually improves test quality. In this paper we aim to fill this gap: By analyzing a large dataset of almost 15 million mutants, we investigate how these mutants influenced developers over time, and how these mutants relate to real faults. Our analyses suggest that developers using mutation testing write more tests, and actively improve their test suites with high quality tests such that fewer mutants remain. By analyzing a dataset of past fixes of real high-priority faults, our analyses further provide evidence that mutants are indeed coupled with real faults. In other words, had mutation testing been used for the changes introducing the faults, it would have reported a live mutant that could have prevented the bug.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115908480","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: 33

Automatic Web Testing Using Curiosity-Driven Reinforcement Learning 使用好奇心驱动的强化学习的自动Web测试

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) Pub Date : 2021-03-10 DOI: 10.1109/ICSE43902.2021.00048

Yan Zheng, Yi Liu, Xiaofei Xie, Yepang Liu, L. Ma, Jianye Hao, Yang Liu

{"title":"Automatic Web Testing Using Curiosity-Driven Reinforcement Learning","authors":"Yan Zheng, Yi Liu, Xiaofei Xie, Yepang Liu, L. Ma, Jianye Hao, Yang Liu","doi":"10.1109/ICSE43902.2021.00048","DOIUrl":"https://doi.org/10.1109/ICSE43902.2021.00048","url":null,"abstract":"Web testing has long been recognized as a notoriously difficult task. Even nowadays, web testing still mainly relies on manual efforts in many cases while automated web testing is still far from achieving human-level performance. Key challenges include dynamic content update and deep bugs hiding under complicated user interactions and specific input values, which can only be triggered by certain action sequences in the huge space of all possible sequences. In this paper, we propose WebExplor, an automatic end-to-end web testing framework, to achieve an adaptive exploration of web applications. WebExplor adopts a curiosity-driven reinforcement learning to generate high-quality action sequences (test cases) with temporal logical relations. Besides, WebExplor incrementally builds an automaton during the online testing process, which acts as the high-level guidance to further improve the testing efficiency. We have conducted comprehensive evaluations on six real-world projects, a commercial SaaS web application, and performed an in-the-wild study of the top 50 web applications in the world. The results demonstrate that in most cases WebExplor can achieve significantly higher failure detection rate, code coverage and efficiency than existing state-of-the-art web testing techniques. WebExplor also detected 12 previously unknown failures in the commercial web application, which have been confirmed and fixed by the developers. Furthermore, our in-the-wild study further uncovered 3,466 exceptions and errors.","PeriodicalId":305167,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116331281","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: 41