2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)最新文献

ICoq: Regression proof selection for large-scale verification projects ICoq:大规模验证项目的回归证明选择

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115630

Ahmet Çelik, Karl Palmskog, Miloš Gligorić

引用次数: 13

FEMIR: A tool for recommending framework extension examples FEMIR:推荐框架扩展示例的工具

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115713

M. Asaduzzaman, C. Roy, Kevin A. Schneider, Daqing Hou

{"title":"FEMIR: A tool for recommending framework extension examples","authors":"M. Asaduzzaman, C. Roy, Kevin A. Schneider, Daqing Hou","doi":"10.1109/ASE.2017.8115713","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115713","url":null,"abstract":"Software frameworks enable developers to reuse existing well tested functionalities instead of taking the burden of implementing everything from scratch. However, to meet application specific requirements, the frameworks need to be customized via extension points. This is often done by passing a framework related object as an argument to an API call. To enable such customizations, the object can be created by extending a framework class, implementing an interface, or changing the properties of the object via API calls. However, it is both a common and non-trivial task to find all the details related to the customizations. In this paper, we present a tool, called FEMIR, that utilizes partial program analysis and graph mining technique to detect, group, and rank framework extension examples. The tool extends existing code completion infrastructure to inform developers about customization choices, enabling them to browse through extension points of a framework, and frequent usages of each point in terms of code examples. A video demo is made available at https://asaduzzamanparvez.wordpress.com/femir.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117146501","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: 7

In-memory fuzzing for binary code similarity analysis 二进制代码相似度分析的内存模糊分析

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115645

Shuai Wang, Dinghao Wu

{"title":"In-memory fuzzing for binary code similarity analysis","authors":"Shuai Wang, Dinghao Wu","doi":"10.1109/ASE.2017.8115645","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115645","url":null,"abstract":"Detecting similar functions in binary executables serves as a foundation for many binary code analysis and reuse tasks. By far, recognizing similar components in binary code remains a challenge. Existing research employs either static or dynamic approaches to capture program syntax or semantics-level features for comparison. However, there exist multiple design limitations in previous work, which result in relatively high cost, low accuracy and scalability, and thus severely impede their practical use. In this paper, we present a novel method that leverages in-memory fuzzing for binary code similarity analysis. Our prototype tool IMF-SIM applies in-memory fuzzing to launch analysis towards every function and collect traces of different kinds of program behaviors. The similarity score of two behavior traces is computed according to their longest common subsequence. To compare two functions, a feature vector is generated, whose elements are the similarity scores of the behavior trace-level comparisons. We train a machine learning model through labeled feature vectors; later, for a given feature vector by comparing two functions, the trained model gives a final score, representing the similarity score of the two functions. We evaluate IMF-SIM against binaries compiled by different compilers, optimizations, and commonly-used obfuscation methods, in total over one thousand binary executables. Our evaluation shows that IMF-SIM notably outperforms existing tools with higher accuracy and broader application scopes.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127464391","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: 53

Sketch-guided GUI test generation for mobile applications 为移动应用程序生成草图引导的GUI测试

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115616

Chucheng Zhang, Haoliang Cheng, Enyi Tang, Xin Chen, Lei Bu, Xuandong Li

{"title":"Sketch-guided GUI test generation for mobile applications","authors":"Chucheng Zhang, Haoliang Cheng, Enyi Tang, Xin Chen, Lei Bu, Xuandong Li","doi":"10.1109/ASE.2017.8115616","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115616","url":null,"abstract":"Mobile applications with complex GUIs are very popular today. However, generating test cases for these applications is often tedious professional work. On the one hand, manually designing and writing elaborate GUI scripts requires expertise. On the other hand, generating GUI scripts with record and playback techniques usually depends on repetitive work that testers need to interact with the application over and over again, because only one path is recorded in an execution. Automatic GUI testing focuses on exploring combinations of GUI events. As the number of combinations is huge, it is still necessary to introduce a test interface for testers to reduce its search space. This paper presents a sketch-guided GUI test generation approach for testing mobile applications, which provides a simple but expressive interface for testers to specify their testing purposes. Testers just need to draw a few simple strokes on the screenshots. Then our approach translates the strokes to a testing model and initiates a model-based automatic GUI testing. We evaluate our sketch-guided approach on a few real-world Android applications collected from the literature. The results show that our approach can achieve higher coverage than existing automatic GUI testing techniques with just 10-minute sketching for an application.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124986633","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

FiB: Squeezing loop invariants by interpolation between forward/backward predicate transformers FiB:通过前向/后向谓词转换器之间的插值压缩循环不变量

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115690

Shang-Wei Lin, Jun Sun, Hao Xiao, Yang Liu, David Sanán, Henri Hansen

引用次数: 19

Predicting relevance of change recommendations 预测变更建议的相关性

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115680

Thomas Rolfsnes, L. Moonen, D. Binkley

{"title":"Predicting relevance of change recommendations","authors":"Thomas Rolfsnes, L. Moonen, D. Binkley","doi":"10.1109/ASE.2017.8115680","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115680","url":null,"abstract":"Software change recommendation seeks to suggest artifacts (e.g., files or methods) that are related to changes made by a developer, and thus identifies possible omissions or next steps. While one obvious challenge for recommender systems is to produce accurate recommendations, a complimentary challenge is to rank recommendations based on their relevance. In this paper, we address this challenge for recommendation systems that are based on evolutionary coupling. Such systems use targeted association-rule mining to identify relevant patterns in a software system's change history. Traditionally, this process involves ranking artifacts using interestingness measures such as confidence and support. However, these measures often fall short when used to assess recommendation relevance. We propose the use of random forest classification models to assess recommendation relevance. This approach improves on past use of various interestingness measures by learning from previous change recommendations. We empirically evaluate our approach on fourteen open source systems and two systems from our industry partners. Furthermore, we consider complimenting two mining algorithms: Co-Change and Tarmaq. The results find that random forest classification significantly outperforms previous approaches, receives lower Brier scores, and has superior trade-off between precision and recall. The results are consistent across software system and mining algorithm.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124111105","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

Towards API-specific automatic program repair 针对api的自动程序修复

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115721

Sebastian Nielebock

引用次数: 2

Parsimony: An IDE for example-guided synthesis of lexers and parsers Parsimony:用于示例指导的词法分析器和解析器合成的IDE

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115692

Alan Leung, Sorin Lerner

引用次数: 3

SimplyDroid: Efficient event sequence simplification for android application SimplyDroid: android应用程序的高效事件序列简化

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115643

Bo Jiang, Yuxuan Wu, Teng Li, W. Chan

引用次数: 9

TiQi: A natural language interface for querying software project data TiQi:用于查询软件项目数据的自然语言接口

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2017-10-30 DOI: 10.1109/ASE.2017.8115714

Jinfeng Lin, Yalin Liu, Jin Guo, J. Cleland-Huang, Will Goss, Wenchuang Liu, Sugandha Lohar, Natawut Monaikul, A. Rasin

{"title":"TiQi: A natural language interface for querying software project data","authors":"Jinfeng Lin, Yalin Liu, Jin Guo, J. Cleland-Huang, Will Goss, Wenchuang Liu, Sugandha Lohar, Natawut Monaikul, A. Rasin","doi":"10.1109/ASE.2017.8115714","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115714","url":null,"abstract":"Software projects produce large quantities of data such as feature requests, requirements, design artifacts, source code, tests, safety cases, release plans, and bug reports. If leveraged effectively, this data can be used to provide project intelligence that supports diverse software engineering activities such as release planning, impact analysis, and software analytics. However, project stakeholders often lack skills to formulate complex queries needed to retrieve, manipulate, and display the data in meaningful ways. To address these challenges we introduce TiQi, a natural language interface, which allows users to express software-related queries verbally or written in natural language. TiQi is a web-based tool. It visualizes available project data as a prompt to the user, accepts Natural Language (NL) queries, transforms those queries into SQL, and then executes the queries against a centralized or distributed database. Raw data is stored either directly in the database or retrieved dynamically at runtime from case tools and repositories such as Github and Jira. The transformed query is visualized back to the user as SQL and augmented UML, and raw data results are returned. Our tool demo can be found on YouTube at the following link:http://tinyurl.com/TIQIDemo.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114141352","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: 14