2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)最新文献_第4页

On the Reliability of Coverage-Based Fuzzer Benchmarking 基于覆盖的模糊基准测试可靠性研究

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510230

Marcel Böhme, László Szekeres, Jonathan Metzman

引用次数: 48

PUS: A Fast and Highly Efficient Solver for Inclusion-based Pointer Analysis 基于包含的指针分析的快速高效求解器

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510075

Peiming Liu, Yanze Li, Bradley Swain, Jeff Huang

{"title":"PUS: A Fast and Highly Efficient Solver for Inclusion-based Pointer Analysis","authors":"Peiming Liu, Yanze Li, Bradley Swain, Jeff Huang","doi":"10.1145/3510003.3510075","DOIUrl":"https://doi.org/10.1145/3510003.3510075","url":null,"abstract":"A crucial performance bottleneck in most interprocedural static analyses is solving pointer analysis constraints. We present Pus, a highly efficient solver for inclusion-based pointer analysis. At the heart of Pus is a new constraint solving algorithm that signifi-cantly advances the state-of-the-art. Unlike the existing algorithms (i.e., wave and deep propagation) which construct a holistic constraint graph, at each stage Pus only considers partial constraints that causally affect the final fixed-point computation. In each iteration Pus extracts a small causality subgraph and it guarantees that only processing the causality subgraph is sufficient to reach the same global fixed point. Our extensive evaluation of Pus on a wide range of real-world large complex programs yields highly promising results. Pus is able to analyze millions of lines of code such as PostgreSQL in 10 minutes on a commodity laptop. On average, Pus is more than 7x faster in solving context-sensitive constraints, and more than 2x faster in solving context-insensitive constraints compared to the state of the art wave and deep propagation algorithms. Moreover, Pus has been used to find tens of previous unknown bugs in high-profile codebases including Linux, Redis, and Memcached.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123968330","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: 0

PREACH: A Heuristic for Probabilistic Reachability to Identify Hard to Reach Statements 用启发式的概率可达性来识别难以达到的语句

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510227

Seemanta Saha, M. Downing, Tegan Brennan, T. Bultan

{"title":"PREACH: A Heuristic for Probabilistic Reachability to Identify Hard to Reach Statements","authors":"Seemanta Saha, M. Downing, Tegan Brennan, T. Bultan","doi":"10.1145/3510003.3510227","DOIUrl":"https://doi.org/10.1145/3510003.3510227","url":null,"abstract":"We present a heuristic for approximating the likelihood of reaching a given program statement using 1) branch selectivity (representing the percentage of values that satisfy a branch condition), which we compute using model counting, 2) dependency analysis, which we use to identify input-dependent branch conditions that influence statement reachability, 3) abstract interpretation, which we use to identify the set of values that reach a branch condition, and 4) a discrete-time Markov chain model, which we construct to capture the control flow structure of the program together with the selectivity of each branch. Our experiments indicate that our heuristic-based probabilistic reachability analysis tool PReach can identify hard to reach statements with high precision and accuracy in benchmarks from software verification and testing competitions, Apache Commons Lang, and the DARPA STAC program. We provide a detailed comparison with probabilistic symbolic execution and statistical symbolic execution for the purpose of identifying hard to reach statements. PREACH achieves comparable precision and accuracy to both probabilistic and statistical symbolic execution for bounded execution depth and better precision and accuracy when execution depth is unbounded and the number of program paths grows exponentially. Moreover, PReach is more scalable than both probabilistic and statistical symbolic execution.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128508543","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: 5

Automatic Detection of Performance Bugs in Database Systems using Equivalent Queries 使用等效查询的数据库系统性能错误自动检测

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510093

Xinyu Liu, Qi Zhou, Joy Arulraj, A. Orso

{"title":"Automatic Detection of Performance Bugs in Database Systems using Equivalent Queries","authors":"Xinyu Liu, Qi Zhou, Joy Arulraj, A. Orso","doi":"10.1145/3510003.3510093","DOIUrl":"https://doi.org/10.1145/3510003.3510093","url":null,"abstract":"Because modern data-intensive applications rely heavily on database systems (DBMSs), developers extensively test these systems to elim-inate bugs that negatively affect functionality. Besides functional bugs, however, there is another important class of faults that negatively affect the response time of a DBMS, known as performance bugs. Despite their potential impact on end-user experience, performance bugs have received considerably less attention than functional bugs. To fill this gap, we present Amoeba, a technique and tool for automatically detecting performance bugs in DBMSs. The core idea behind Amoeba is to construct semantically equivalent query pairs, run both queries on the DBMS under test, and compare their response time. If the queries exhibit significantly different response times, that indicates the possible presence of a performance bug in the DBMS. To construct equivalent queries, we propose to use a set of structure and expression mutation rules especially targeted at un-covering performance bugs. We also introduce feedback mechanisms for improving the effectiveness and efficiency of the approach. We evaluate Amoeba on two widely-used DBMSs, namely PostgreSQL and CockroachDB, with promising results: Amoeba has so far dis-covered 39 potential performance bugs, among which developers have already confirmed 6 bugs and fixed 5 bugs.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124354512","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

Testing Time Limits in Screener Questions for Online Surveys with Programmers 测试时间限制在筛选问题与程序员在线调查

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510223

A. Danilova, S. Horstmann, Matthew Smith, Alena Naiakshina

引用次数: 3

FADATest: Fast and Adaptive Performance Regression Testing of Dynamic Binary Translation Systems 动态二进制翻译系统的快速自适应性能回归测试

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510169

Jin Yu Wu, Jian Dong, Ruili Fang, Wen Zhang, Wenwen Wang, Decheng Zuo

{"title":"FADATest: Fast and Adaptive Performance Regression Testing of Dynamic Binary Translation Systems","authors":"Jin Yu Wu, Jian Dong, Ruili Fang, Wen Zhang, Wenwen Wang, Decheng Zuo","doi":"10.1145/3510003.3510169","DOIUrl":"https://doi.org/10.1145/3510003.3510169","url":null,"abstract":"Dynamic binary translation (DBT) is the cornerstone of many im-portant applications. In practice, however, it is quite difficult to maintain the performance efficiency of a DBT system due to its inherent complexity. Although performance regression testing is an effective approach to detect potential performance regression issues, it is not easy to apply performance regression testing to DBT sys-tems, because of the natural differences between DBT systems and common software systems and the limited availability of effective test programs. In this paper, we present FADATest, which devises several novel techniques to address these challenges. Specifically, FADATest automatically generates adaptable test programs from existing real benchmark programs of DBT systems according to the runtime characteristics of the benchmarks. The test programs can then be used to achieve highly efficient and adaptive performance regression testing of DBT systems. We have implemented a proto-type of FADATest. Experimental results show that FADATest can successfully uncover the same performance regression issues across the evaluated versions of two popular DBT systems, QEMU and Valgrind, as the original benchmark programs. Moreover, the testing efficiency is improved significantly on two different hardware platforms powered by x86-64 and AArch64, respectively.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131940271","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: 2

PROMAL: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning 基于程序分析和机器学习的Android精确窗口转换图

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510037

Changlin Liu, Hanlin Wang, Tianming Liu, Diandian Gu, Yun Ma, Haoyu Wang, Xusheng Xiao

{"title":"PROMAL: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning","authors":"Changlin Liu, Hanlin Wang, Tianming Liu, Diandian Gu, Yun Ma, Haoyu Wang, Xusheng Xiao","doi":"10.1145/3510003.3510037","DOIUrl":"https://doi.org/10.1145/3510003.3510037","url":null,"abstract":"Mobile apps have been an integral part in our daily life. As these apps become more complex, it is critical to provide automated analysis techniques to ensure the correctness, security, and performance of these apps. A key component for these automated analysis techniques is to create a graphical user interface (GUI) model of an app, i.e., a window transition graph (WTG), that models windows and transitions among the windows. While existing work has provided both static and dynamic analysis to build the WTG for an app, the constructed WTG misses many transitions or contains many infeasible transitions due to the coverage issues of dynamic analysis and over-approximation of the static analysis. We propose ProMal, a “tribrid” analysis that synergistically combines static analysis, dynamic analysis, and machine learning to construct a precise WTG. Specifically, ProMal first applies static analysis to build a static WTG, and then applies dynamic analysis to verify the transitions in the static WTG. For the unverified transitions, ProMal further provides machine learning techniques that leverage runtime information (i.e., screenshots, UI layouts, and text information) to predict whether they are feasible transitions. Our evaluations on 40 real-world apps demonstrate the superiority of ProMal in building WTGs over static analysis, dynamic analysis, and machine learning techniques when they are applied separately.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128239533","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: 0

DEAR: A Novel Deep Learning-based Approach for Automated Program Repair DEAR:一种新的基于深度学习的自动程序修复方法

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510177

Yi Li, Shaohua Wang, T. Nguyen

{"title":"DEAR: A Novel Deep Learning-based Approach for Automated Program Repair","authors":"Yi Li, Shaohua Wang, T. Nguyen","doi":"10.1145/3510003.3510177","DOIUrl":"https://doi.org/10.1145/3510003.3510177","url":null,"abstract":"The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. We present DEAR, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to one or mul-tiple consecutive statements in one or multiple hunks of code. We first design a novel fault localization (FL) technique for multi-hunk, multi-statement fixes that combines traditional spectrum-based (SB) FL with deep learning and data-flow analysis. It takes the buggy statements returned by the SBFL model, detects the buggy hunks to be fixed at once, and expands a buggy statement $s$ in a hunk to include other suspicious statements around s. We design a two-tier, tree-based LSTM model that incorporates cycle training and uses a divide-and-conquer strategy to learn proper code transformations for fixing multiple statements in the suitable fixing context consisting of surrounding subtrees. We conducted several experiments to evaluate DEAR on three datasets: Defects4J (395 bugs), BigFix (+26k bugs), and CPatMiner (+44k bugs). On Defects4J dataset, DEAR outperforms the baselines from 42%-683% in terms of the number of auto-fixed bugs with only the top-1 patches. On BigFix dataset, it fixes 31–145 more bugs than existing DL-based APR models with the top-1 patches. On CPatMiner dataset, among 667 fixed bugs, there are 169 (25.3%) multi-hunk/multi-statement bugs. DEAR fixes 71 and 164 more bugs, including 52 and 61 more multi-hunk/multi-statement bugs, than the state-of-the-art, DL-based APR models.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129739817","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

Controlled Concurrency Testing via Periodical Scheduling 通过周期调度控制并发测试

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510178

Cheng Wen, Mengda He, Bohao Wu, Zhiwu Xu, S. Qin

引用次数: 8

Practitioners' Expectations on Automated Code Comment Generation 实践者对自动代码注释生成的期望

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510152

Xing Hu, Xin Xia, D. Lo, Zhiyuan Wan, Qiuyuan Chen, Thomas Zimmermann

{"title":"Practitioners' Expectations on Automated Code Comment Generation","authors":"Xing Hu, Xin Xia, D. Lo, Zhiyuan Wan, Qiuyuan Chen, Thomas Zimmermann","doi":"10.1145/3510003.3510152","DOIUrl":"https://doi.org/10.1145/3510003.3510152","url":null,"abstract":"Good comments are invaluable assets to software projects, as they help developers understand and maintain projects. However, due to some poor commenting practices, comments are often missing or inconsistent with the source code. Software engineering practitioners often spend a significant amount of time and effort reading and understanding programs without or with poor comments. To counter this, researchers have proposed various techniques to au-tomatically generate code comments in recent years, which can not only save developers time writing comments but also help them better understand existing software projects. However, it is unclear whether these techniques can alleviate comment issues and whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by interviewing and surveying practitioners about their expectations of research in code comment generation. We then compared what practitioners need and the current state-of-the-art research by performing a literature review of papers on code comment generation techniques pub-lished in the premier publication venues from 2010 to 2020. From this comparison, we highlighted the directions where researchers need to put effort to develop comment generation techniques that matter to practitioners.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"57 9-10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132287154","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