2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)最新文献_第4页

XSTRESSOR : Automatic Generation of Large-Scale Worst-Case Test Inputs by Inferring Path Conditions XSTRESSOR:通过推断路径条件自动生成大规模最坏情况测试输入

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/ICST.2019.00011

Charitha Saumya, Jinkyu Koo, Milind Kulkarni, S. Bagchi

{"title":"XSTRESSOR : Automatic Generation of Large-Scale Worst-Case Test Inputs by Inferring Path Conditions","authors":"Charitha Saumya, Jinkyu Koo, Milind Kulkarni, S. Bagchi","doi":"10.1109/ICST.2019.00011","DOIUrl":"https://doi.org/10.1109/ICST.2019.00011","url":null,"abstract":"An important part of software testing is generation of worst-case test inputs, which exercise a program under extreme loads. For such a task, symbolic execution is a useful tool with its capability to reason about all possible execution paths of a program, including the one with the worst case behavior. However, symbolic execution suffers from the path explosion problem and frequent calls to a constraint solver, which make it impractical to be used at a large scale. To address the issue, this paper presents XSTRESSOR that is able to generate test inputs that can run specific loops in a program with the worst-case complexity in a large scale. XSTRESSOR synthetically generates the path condition for the large-scale, worst-case execution from a predictive model that is built from a set of small scale tests. XSTRESSOR avoids the scaling problem of prior techniques by limiting full-blown symbolic execution and run-time calls to constraint solver to small scale tests only. We evaluate XSTRESSOR against WISE and SPF-WCA, the most closely related tools to generate worst-case test inputs. Results show that XSTRESSOR can generate the test inputs faster than WISE and SPF-WCA, and also scale to much larger input sizes.","PeriodicalId":446827,"journal":{"name":"2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134476117","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

TestSage: Regression Test Selection for Large-Scale Web Service Testing 大规模Web服务测试的回归测试选择

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/ICST.2019.00052

Hua Zhong, Lingming Zhang, S. Khurshid

{"title":"TestSage: Regression Test Selection for Large-Scale Web Service Testing","authors":"Hua Zhong, Lingming Zhang, S. Khurshid","doi":"10.1109/ICST.2019.00052","DOIUrl":"https://doi.org/10.1109/ICST.2019.00052","url":null,"abstract":"Regression testing is an important but expensive activity in software development. Among various types of tests, web service tests are usually one of the most expensive (due to network communications) but widely adopted types of tests in commercial software development. Regression test selection (RTS) aims to reduce the number of tests which need to be retested by only running tests that are affected by code changes. Although a large number of RTS techniques have been proposed in the past few decades, these techniques have not been adopted on large-scale web service testing. This is because most existing RTS techniques either require direct code dependency between tests and code under test or cannot be applied on large scale systems with enough efficiency. In this paper, we present a novel RTS technique, TestSage, that performs RTS for web service tests on large scale commercial software. With a small overhead, TestSage is able to collect fine grained (function level) dependency between test and service under test that do not directly depend on each other. TestSage has also been successfully applied to large complex systems with over a million functions. We conducted experiments of TestSage on a large scale backend service at Google. Experimental results show that TestSage reduces 34% of testing time when running all AEC (Analysis, Execution and Collection) phases, 50% of testing time while running without collection phase. TestSage has been integrated with internal testing framework at Google and runs day-to-day at the company.","PeriodicalId":446827,"journal":{"name":"2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129072367","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: 17

An Empirical Assessment of Machine Learning Approaches for Triaging Reports of a Java Static Analysis Tool 一个Java静态分析工具的分类报告的机器学习方法的经验评估

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/ICST.2019.00036

Ugur Koc, Shiyi Wei, J. Foster, Marine Carpuat, A. Porter

{"title":"An Empirical Assessment of Machine Learning Approaches for Triaging Reports of a Java Static Analysis Tool","authors":"Ugur Koc, Shiyi Wei, J. Foster, Marine Carpuat, A. Porter","doi":"10.1109/ICST.2019.00036","DOIUrl":"https://doi.org/10.1109/ICST.2019.00036","url":null,"abstract":"Despite their ability to detect critical bugs in software, developers consider high false positive rates to be a key barrier to using static analysis tools in practice. To improve the usability of these tools, researchers have recently begun to apply machine learning techniques to classify and filter false positive analysis reports. Although initial results have been promising, the long-term potential and best practices for this line of research are unclear due to the lack of detailed, large-scale empirical evaluation. To partially address this knowledge gap, we present a comparative empirical study of four machine learning techniques, namely hand-engineered features, bag of words, recurrent neural networks, and graph neural networks, for classifying false positives, using multiple ground-truth program sets. We also introduce and evaluate new data preparation routines for recurrent neural networks and node representations for graph neural networks, and show that these routines can have a substantial positive impact on classification accuracy. Overall, our results suggest that recurrent neural networks (which learn over a program's source code) outperform the other subject techniques, although interesting tradeoffs are present among all techniques. Our observations provide insight into the future research needed to speed the adoption of machine learning approaches in practice.","PeriodicalId":446827,"journal":{"name":"2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128032685","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: 27

PySE: Automatic Worst-Case Test Generation by Reinforcement Learning PySE:通过强化学习自动生成最坏情况测试

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/ICST.2019.00023

Jinkyu Koo, Charitha Saumya, Milind Kulkarni, S. Bagchi

{"title":"PySE: Automatic Worst-Case Test Generation by Reinforcement Learning","authors":"Jinkyu Koo, Charitha Saumya, Milind Kulkarni, S. Bagchi","doi":"10.1109/ICST.2019.00023","DOIUrl":"https://doi.org/10.1109/ICST.2019.00023","url":null,"abstract":"Stress testing is an important task in software testing, which examines the behavior of a program under a heavy load. Symbolic execution is a useful tool to find out the worst-case input values for the stress testing. However, symbolic execution does not scale to a large program, since the number of paths to search grows exponentially with an input size. So far, such a scalability issue has been mostly managed by pruning out unpromising paths in the middle of searching based on heuristics, but this kind of work easily eliminates the true worst case as well, providing sub-optimal one only. Another way to achieve scalability is to learn a branching policy of worst-case complexity from small scale tests and apply it to a large scale. However, use cases of such a method are restricted to programs whose worst-case branching policy has a simple pattern. To address such limitations, we propose PySE that uses symbolic execution to collect the behaviors of a given branching policy, and updates the policy using a reinforcement learning approach through multiple executions. PySE's branching policy keeps evolving in a way that the length of an execution path increases in the long term, and ultimately reaches the worst-case complexity. PySE can also learn the worst-case branching policy of a complex or irregular pattern, using an artificial neural network in a fully automatic way. Experiment results demonstrate that PySE can effectively find a path of worst-case complexity for various Python benchmark programs and scales.","PeriodicalId":446827,"journal":{"name":"2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131136676","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: 20

Message from the General Chairs 主席致辞

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/icst.2019.00005

Srds

引用次数: 0

Poster: Aiding Java Developers with Interactive Fault Localization in Eclipse IDE 海报:帮助Java开发人员在Eclipse IDE中进行交互式故障定位

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/ICST.2019.00045

Gergõ Balogh, Ferenc Horváth, Árpád Beszédes

{"title":"Poster: Aiding Java Developers with Interactive Fault Localization in Eclipse IDE","authors":"Gergõ Balogh, Ferenc Horváth, Árpád Beszédes","doi":"10.1109/ICST.2019.00045","DOIUrl":"https://doi.org/10.1109/ICST.2019.00045","url":null,"abstract":"Spectrum-Based ones are a popular class of Fault Localization (FL) methods among researchers due to their relative simplicity. However, recent studies highlighted some barriers to the wider adoption of the technique in practical settings. One possibility to increase the practical usefulness of related tools is to involve interactivity between the user and the core FL algorithm. In this setting, the developer interacts with the fault localization algorithm by giving feedback on the elements proposed by the algorithm. This way, the proposed elements can be influenced in the hope to reach the faulty element earlier (we call the proposed approach Interactive Fault Localization, or iFL). With this work, we present our recent achievements in this topic. In particular, we overview the basic approach, our preliminary experimentation with user simulation, and the supporting tool for the actual usage of the method, iFL for Eclipse. Our aim is to provide a basis for the investigation of the feasibility and effectiveness of the technique, before moving on to more comprehensive experiments with actual human subjects. We invite researchers for further discussion on the topic, and for that, the method and tool will be made accessible.","PeriodicalId":446827,"journal":{"name":"2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122066094","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

Using Testing to Repair Models 使用测试来修复模型

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/ICST.2019.00061

M. Radavelli

引用次数: 0

Precise Static Happens-Before Analysis for Detecting UAF Order Violations in Android 精确的静态发生-在Android中检测UAF秩序违规之前的分析

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/ICST.2019.00035

Diyu Wu, Jie Liu, Yulei Sui, Shiping Chen, Jingling Xue

{"title":"Precise Static Happens-Before Analysis for Detecting UAF Order Violations in Android","authors":"Diyu Wu, Jie Liu, Yulei Sui, Shiping Chen, Jingling Xue","doi":"10.1109/ICST.2019.00035","DOIUrl":"https://doi.org/10.1109/ICST.2019.00035","url":null,"abstract":"Unlike Java, Android provides a rich set of APIs to support a hybrid concurrency system, which consists of both Java threads and an event queue mechanism for dispatching asynchronous events. In this model, concurrency errors often manifest themselves in the form of order violations. An order violation occurs when two events access the same shared object in an incorrect order, causing unexpected program behaviors (e.g., null pointer dereferences). This paper presents SARD, a static analysis tool for detecting both intra-and inter-thread use-after-free (UAF) order violations, when a pointer is dereferenced (used) after it no longer points to any valid object, through systematic modeling of Android's concurrency mechanism. We propose a new flow-and context-sensitive static happens-before (HB) analysis to reason about the interleavings between two events to effectively identify precise HB relations and eliminate spurious event interleavings. We have evaluated SARD by comparing with NADROID, a state-of-the-art static order violation detection tool for Android. SARD outperforms NADROID in terms of both precision (by reporting three times fewer false alarms than NADROID given the same set of apps used by NADROID) and efficiency (by running two orders of magnitude faster than NADROID).","PeriodicalId":446827,"journal":{"name":"2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130712490","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: 10

Efficient Automated Decomposition of Build Targets at Large-Scale 大规模构建目标的高效自动分解

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/ICST.2019.00055

Lukás Jendele, Markus Schwenk, Diana Cremarenco, I. Janicijevic, M. Rybalkin

{"title":"Efficient Automated Decomposition of Build Targets at Large-Scale","authors":"Lukás Jendele, Markus Schwenk, Diana Cremarenco, I. Janicijevic, M. Rybalkin","doi":"10.1109/ICST.2019.00055","DOIUrl":"https://doi.org/10.1109/ICST.2019.00055","url":null,"abstract":"Large monolithic codebases, such as those used at Google and Facebook, enable engineers to easily share code and allow cross-team collaboration. Such codebases are partitioned into a huge number of libraries, binaries, and tests. However, engineers currently usually have to state the build dependencies between those blocks of functionality manually. One of the possible inefficiencies introduced that way are underutilized libraries, i.e. libraries that provide more functionality than required by the dependent code. This results in slow builds and an increased load on the Continuous Integration System. In this paper, we propose a way to automatically find and decompose underutilized libraries into a set of smaller components, where each component is a standalone library. Our work focuses on decompositions at source file level. While prior work already proposed decompositions when the final number of components was given as an input, we introduce an algorithm, AutoDecomposer, that finds the number of components automatically. In contrast to existing work, we analyze how a decomposition would lower the number of tests triggered by the Continuous Integration System in order to select only those decompositions that provide an impact. We evaluate AutoDecomposer's efficiency by comparing its potential impact to the maximum theoretical impact achievable by applying the most granular decomposition. We conclude that applying AutoDecomposer's decompositions generates 95% of the theoretical maximum test triggering frequency reduction, while only generating 4% as many components for large targets and 30% as many components on average compared to the theoretically most efficient approach.","PeriodicalId":446827,"journal":{"name":"2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132087565","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: 6

[Publisher's information] (发布者的信息)

2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) Pub Date : 2019-04-01 DOI: 10.1109/icst.2019.00065

引用次数: 0