2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)最新文献_第4页

Detecting Runtime Exceptions by Deep Code Representation Learning with Attention-Based Graph Neural Networks 基于注意力的图神经网络深度代码表示学习检测运行时异常

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00053

Rongfang Li, Bihuan Chen, Fengyi Zhang, Chao Sun, Xin Peng

{"title":"Detecting Runtime Exceptions by Deep Code Representation Learning with Attention-Based Graph Neural Networks","authors":"Rongfang Li, Bihuan Chen, Fengyi Zhang, Chao Sun, Xin Peng","doi":"10.1109/saner53432.2022.00053","DOIUrl":"https://doi.org/10.1109/saner53432.2022.00053","url":null,"abstract":"Uncaught runtime exceptions have been recognized as one of the commonest root causes of real-life exception bugs in Java applications. However, existing runtime exception detection techniques rely on symbolic execution or random testing, which may suffer the scalability or coverage problem. Rule-based bug detectors (e.g., SpotBugs) provide limited rule support for runtime exceptions. Inspired by the recent successes in applying deep learning to bug detection, we propose a deep learning-based technique, named Drex, to identify not only the types of runtime exceptions that a method might signal but also the statement scopes that might signal the detected runtime exceptions. It is realized by graph-based code representation learning with (i) a lightweight analysis to construct a joint graph of CFG, DFG and AST for each method without requiring a build environment so as to comprehensively characterize statement syntax and semantics and (ii) an attention-based graph neural network to learn statement embeddings in order to distinguish different types of potentially signaled runtime exceptions with interpretability. Our evaluation on 54,255 methods with caught runtime exceptions and 54,255 methods without caught runtime exceptions from 5,996 GitHub Java projects has indicated that Drex improves baseline approaches by up to 18.2% in exact accuracy and 41.6% in F1-score. Drex detects 20 new uncaught runtime exceptions in 13 real-life pro-jects, 7 of them have been fixed, while none of them is detected by rule-based bug detectors (i.e., SpotBugs and PMD).","PeriodicalId":437520,"journal":{"name":"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133229903","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

What Do We Know About Readability of Test Code? - A Systematic Mapping Study 关于测试代码的可读性我们知道些什么?-系统测绘研究

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00135

D. Winkler, Pirmin Urbanke, R. Ramler

{"title":"What Do We Know About Readability of Test Code? - A Systematic Mapping Study","authors":"D. Winkler, Pirmin Urbanke, R. Ramler","doi":"10.1109/saner53432.2022.00135","DOIUrl":"https://doi.org/10.1109/saner53432.2022.00135","url":null,"abstract":"The readability of software code is a key success criterion for understanding and maintaining software systems and tests. In industry practice, a limited number of guidelines aim for improving and assessing the readability of software (test) code. Although several studies focus on investigating the readability of software code, we observed limited research work that focuses on the readability of software test code. In this paper we focus on systematically investigating the characteristics, factors, and assessment criteria that have an impact on the readability of test code. We build on a Systematic Mapping Study (SMS) to identify key characteristics, factors, and assessment criteria that have an impact on test code readability, legibility, and understandability to support and improve maintenance tasks. The result set includes 16 studies for further analysis. The majority of publications focuses on readability investigations of automatically generated test code (88%), often evaluated with surveys to access the readability of test code (44 %). Although several approaches aim at assessing the readability with focus on isolated factors, a combination of different readability aspects within an assessment framework can help to better assess and justify the readability of test code with focus on improving software and system test maintenance.","PeriodicalId":437520,"journal":{"name":"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133865095","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

Static Analysis Warnings and Automatic Fixing: A Replication for C# Projects 静态分析警告和自动修复:c#项目的复制

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00098

Martin Odermatt, Diego Marcilio, Carlo A. Furia

{"title":"Static Analysis Warnings and Automatic Fixing: A Replication for C# Projects","authors":"Martin Odermatt, Diego Marcilio, Carlo A. Furia","doi":"10.1109/saner53432.2022.00098","DOIUrl":"https://doi.org/10.1109/saner53432.2022.00098","url":null,"abstract":"Static analyzers have become increasingly popular both as developer tools and as subjects of empirical studies. Whereas static analysis tools exist for disparate programming languages, the bulk of the empirical research has focused on the popular Java programming language. In this paper, we investigate to what extent some known results about using static analyzers for Java change when considering C#-another popular object-oriented language. To this end, we combine two replications of previous Java studies. First, we study which static analysis tools are most widely used among C# developers, and which warnings are more commonly reported by these tools on open-source C# projects. Second, we develop and empirically evaluate EagleRepair: a technique to automatically fix code in response to static analysis warnings; this is a replication of our previous work for Java [20]. Our replication indicates, among other things, that 1) static code analysis is fairly popular among C# developers too; 2) Re-Sharper is the most widely used static analyzer for C#; 3) several static analysis rules are commonly violated in both Java and C# projects; 4) automatically generating fixes to static code analysis warnings with good precision is feasible in C#. The EagleRepair tool developed for this research is available as open source.","PeriodicalId":437520,"journal":{"name":"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115791335","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

On the rise and fall of CI services in GitHub 关于GitHub中CI服务的兴衰

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00084

M. Golzadeh, Alexandre Decan, T. Mens

引用次数: 20

iTest: Using coverage measurements to improve test efficiency 测试:使用覆盖度量来提高测试效率

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00133

Stefan Fischer, Denise Rigoni, N. Obrenović

引用次数: 1

Machine Learning Regression Techniques for Test Case Prioritization in Continuous Integration Environment 持续集成环境中测试用例优先级的机器学习回归技术

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00034

Enrique A. da Roza, Jackson A. Prado Lima, Rogério C. Silva, S. Vergilio

{"title":"Machine Learning Regression Techniques for Test Case Prioritization in Continuous Integration Environment","authors":"Enrique A. da Roza, Jackson A. Prado Lima, Rogério C. Silva, S. Vergilio","doi":"10.1109/saner53432.2022.00034","DOIUrl":"https://doi.org/10.1109/saner53432.2022.00034","url":null,"abstract":"Test Case Prioritization (TCP) techniques are a key factor in reducing the regression testing costs even more when Continuous Integration (CI) practices are adopted. TCP approaches based on failure history have been adopted in this context because they are more suitable for CI environment constraints: test budget and test case volatility, that is, test cases may be added or removed over the CI cycles. Promising approaches are based on Reinforcement Learning (RL), which learns with past prioritization, guided by a reward function. In this work, we introduce a TCP approach for CI environments based on the sliding window method, which can be instantiated with different Machine Learning (ML) algorithms. Unlike other ML approaches, it does not require retraining the model to perform the prioritization and any code analysis. As an alternative for the RL approaches, we apply the Random Forest (RF) algorithm and a Long Short Term Memory (LSTM) deep learning network in our evaluation. We use three time budgets and eleven systems. The results show the applicability of the approach considering the prioritization time and the time between the CI cycles. Both algorithms take just a few seconds to execute. The RF algorithm obtained the best performance for more restrictive budgets compared to the RL approaches described in the literature. Considering all systems and budgets, RF reaches Normalized Average Percentage of Faults Detected (NAPFD) values that are the best or statistically equivalent to the best ones in around 72% of the cases, and the LSTM network in 55% of them. Moreover, we discuss some implications of our results for the usage of the algorithms evaluated.","PeriodicalId":437520,"journal":{"name":"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116558765","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: 4

Using Source Code Metrics for Predicting Metamorphic Relations at Method Level 在方法级使用源代码度量来预测变形关系

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/SANER53432.2022.0013

Alejandra Duque-Torres, Dietmar Pfahl, Claus Klammer, Stefan Fischer

{"title":"Using Source Code Metrics for Predicting Metamorphic Relations at Method Level","authors":"Alejandra Duque-Torres, Dietmar Pfahl, Claus Klammer, Stefan Fischer","doi":"10.1109/SANER53432.2022.0013","DOIUrl":"https://doi.org/10.1109/SANER53432.2022.0013","url":null,"abstract":"Metamorphic testing (TM) examines the relations between inputs and outputs of test runs. These relations are known as metamorphic relations (MR). Currently, MRs are handpicked and require in-depth knowledge of the System Under Test (SUT), as well as its problem domain. As a result, the identification and selection of high-quality MRs is a challenge. Kanewala et al. suggested the Predicting Metamorphic Relations (PMR) approach for automatic prediction of applicable MRs picked from a predefined list. PMR is based on a Support Vector Machine (SVM) model using features derived from the Control Flow Graphs (CFGs) of 100 Java methods. The original study of Kanewala et al. showed encouraging results, but developing classification models from CFG-related features is costly. In this paper, we aim at developing a PMR approach that is less costly without losing performance. We complement the original PMR approach by considering other than CFG-related features. We define 21 features that can be directly extracted from source code and build several classifiers, including SVM models. Our results indicate that using the original CFG-based method-level features, in particular for a SVM with random walk kernel (RWK), achieve better predictions in terms of AUC-ROC for most of the candidate MRs than our models. However, for one of the candidate MRs, using source code features achieved the best AUC-ROC result (greater than 0.8).","PeriodicalId":437520,"journal":{"name":"2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121160386","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: 1

Accessing the Presentation of Causal Graphs and an Application of Gestalt Principles with Eye Tracking 眼动追踪中因果图的呈现及格式塔原理的应用

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00153

Lisa Grabinger, Florian Hauser, J. Mottok

引用次数: 2

MLOps for evolvable AI intensive software systems 可进化AI密集型软件系统的mlop

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00155

Sergio Moreschini, Francesco Lomio, David Hästbacka, D. Taibi

引用次数: 9

Message from the General Chair and Program Co-Chairs SANER 2022 SANER 2022总主席和项目联合主席的致辞

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) Pub Date : 2022-03-01 DOI: 10.1109/saner53432.2022.00006

引用次数: 0