Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering最新文献_第7页

ARdoc: app reviews development oriented classifier ARdoc:面向应用评论开发的分类器

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2983938

Sebastiano Panichella, Andrea Di Sorbo, Emitzá Guzmán, C. A. Visaggio, G. Canfora, H. Gall

{"title":"ARdoc: app reviews development oriented classifier","authors":"Sebastiano Panichella, Andrea Di Sorbo, Emitzá Guzmán, C. A. Visaggio, G. Canfora, H. Gall","doi":"10.1145/2950290.2983938","DOIUrl":"https://doi.org/10.1145/2950290.2983938","url":null,"abstract":"Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present ARdoc, a tool which combines three techniques: (1) Natural Language Parsing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify useful feedback contained in app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrates that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and F-Measure (ranging between 84% and 89%). While evaluating our tool developers of our study confirmed the usefulness of ARdoc in extracting important maintenance tasks for their mobile applications. Demo URL: https://youtu.be/Baf18V6sN8E Demo Web Page: http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.html","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83097347","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: 95

Parallel data race detection for task parallel programs with locks 带锁的任务并行程序的并行数据竞争检测

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2950329

Adarsh Yoga, Santosh Nagarakatte, Aarti Gupta

{"title":"Parallel data race detection for task parallel programs with locks","authors":"Adarsh Yoga, Santosh Nagarakatte, Aarti Gupta","doi":"10.1145/2950290.2950329","DOIUrl":"https://doi.org/10.1145/2950290.2950329","url":null,"abstract":"Programming with tasks is a promising approach to write performance portable parallel code. In this model, the programmer explicitly specifies tasks and the task parallel runtime employs work stealing to distribute tasks among threads. Similar to multithreaded programs, task parallel programs can also exhibit data races. Unfortunately, prior data race detectors for task parallel programs either run the program serially or do not handle locks, and/or detect races only in the schedule observed by the analysis. This paper proposes PTRacer, a parallel on-the-fly data race detector for task parallel programs that use locks. PTRacer detects data races not only in the observed schedule but also those that can happen in other schedules (which are permutations of the memory operations in the observed schedule) for a given input. It accomplishes the above goal by leveraging the dynamic execution graph of a task parallel execution to determine whether two accesses can happen in parallel and by maintaining constant amount of access history metadata with each distinct set of locks held for each shared memory location. To detect data races (beyond the observed schedule) in programs with branches sensitive to scheduling decisions, we propose static compiler instrumentation that records memory accesses that will be executed in the other path with simple branches. PTRacer has performance overheads similar to the state-of-the-art race detector for task parallel programs, SPD3, while detecting more races in programs with locks.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83329166","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: 23

Model, execute, and deploy: answering the hard questions in end-user programming (showcase) 建模、执行和部署:回答最终用户编程中的难题(展示)

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2994158

S. Huang

引用次数: 0

Automated change impact analysis between SysML models of requirements and design 需求和设计的SysML模型之间的自动变更影响分析

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2950293

S. Nejati, M. Sabetzadeh, Chetan Arora, L. Briand, Felix Mandoux

{"title":"Automated change impact analysis between SysML models of requirements and design","authors":"S. Nejati, M. Sabetzadeh, Chetan Arora, L. Briand, Felix Mandoux","doi":"10.1145/2950290.2950293","DOIUrl":"https://doi.org/10.1145/2950290.2950293","url":null,"abstract":"An important activity in systems engineering is analyzing how a change in requirements will impact the design of a system. Performing this analysis manually is expensive, particularly for complex systems. In this paper, we propose an approach to automatically identify the impact of requirements changes on system design, when the requirements and design elements are expressed using models. We ground our approach on the Systems Modeling Language (SysML) due to SysML's increasing use in industrial applications. Our approach has two steps: For a given change, we first apply a static slicing algorithm to extract an estimated set of impacted model elements. Next, we rank the elements of the resulting set according to a quantitative measure designed to predict how likely it is for each element to be impacted. The measure is computed using Natural Language Processing (NLP) applied to the textual content of the elements. Engineers can then inspect the ranked list of elements and identify those that are actually impacted. We evaluate our approach on an industrial case study with 16 real-world requirements changes. Our results suggest that, using our approach, engineers need to inspect on average only 4.8% of the entire design in order to identify the actually-impacted elements. We further show that our results consistently improve when our analysis takes into account both structural and behavioral diagrams rather than only structural ones, and the natural-language content of the diagrams in addition to only their structural and behavioral content.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73193955","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: 22

Reasoning with imprecise privacy preferences 用不精确的隐私偏好推理

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2983982

Inah Omoronyia

引用次数: 10

Discovering bug patterns in JavaScript 发现JavaScript中的bug模式

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2950308

Quinn Hanam, Fernando Brito, A. Mesbah

{"title":"Discovering bug patterns in JavaScript","authors":"Quinn Hanam, Fernando Brito, A. Mesbah","doi":"10.1145/2950290.2950308","DOIUrl":"https://doi.org/10.1145/2950290.2950308","url":null,"abstract":"JavaScript has become the most popular language used by developers for client and server side programming. The language, however, still lacks proper support in the form of warnings about potential bugs in the code. Most bug finding tools in use today cover bug patterns that are discovered by reading best practices or through developer intuition and anecdotal observation. As such, it is still unclear which bugs happen frequently in practice and which are important for developers to be fixed. We propose a novel semi-automatic technique, called BugAID, for discovering the most prevalent and detectable bug patterns. BugAID is based on unsupervised machine learning using language-construct-based changes distilled from AST differencing of bug fixes in the code. We present a large-scale study of common bug patterns by mining 105K commits from 134 server-side JavaScript projects. We discover 219 bug fixing change types and discuss 13 pervasive bug patterns that occur across multiple projects and can likely be prevented with better tool support. Our findings are useful for improving tools and techniques to prevent common bugs in JavaScript, guiding tool integration for IDEs, and making developers aware of common mistakes involved with programming in JavaScript.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77318332","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

Discovering additional violations of Java API invariants 发现对Java API不变量的其他违反

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2983977

Waylon Huang

引用次数: 0

Regression testing of web applications using Record/Replay tools 使用Record/Replay工具对web应用程序进行回归测试

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2983942

Mouna Hammoudi

引用次数: 2

Proteus: computing disjunctive loop summary via path dependency analysis Proteus:通过路径依赖分析计算析取循环摘要

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2950340

Xiaofei Xie, Bihuan Chen, Yang Liu, Wei Le, Xiaohong Li

{"title":"Proteus: computing disjunctive loop summary via path dependency analysis","authors":"Xiaofei Xie, Bihuan Chen, Yang Liu, Wei Le, Xiaohong Li","doi":"10.1145/2950290.2950340","DOIUrl":"https://doi.org/10.1145/2950290.2950340","url":null,"abstract":"Loops are challenging structures for program analysis, especially when loops contain multiple paths with complex interleaving executions among these paths. In this paper, we first propose a classification of multi-path loops to understand the complexity of the loop execution, which is based on the variable updates on the loop conditions and the execution order of the loop paths. Secondly, we propose a loop analysis framework, named Proteus, which takes a loop program and a set of variables of interest as inputs and summarizes path-sensitive loop effects on the variables. The key contribution is to use a path dependency automaton (PDA) to capture the execution dependency between the paths. A DFS-based algorithm is proposed to traverse the PDA to summarize the effect for all feasible executions in the loop. The experimental results show that Proteus is effective in three applications: Proteus can 1) compute a more precise bound than the existing loop bound analysis techniques; 2) significantly outperform state-of-the-art tools for loop verification; and 3) generate test cases for deep loops within one second, while KLEE and Pex either need much more time or fail.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73462939","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: 43

Python predictive analysis for bug detection 用于bug检测的Python预测分析

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering Pub Date : 2016-11-01 DOI: 10.1145/2950290.2950357

Zhaogui Xu, Peng Liu, X. Zhang, Baowen Xu

引用次数: 23