2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)最新文献_第3页

SPICA: A Methodology for Reviewing and Analysing Fault Localisation Techniques SPICA:一种回顾和分析故障定位技术的方法

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.1109/ICSME52107.2021.00039

Xiao-Yi Zhang, Mingyue Jiang

{"title":"SPICA: A Methodology for Reviewing and Analysing Fault Localisation Techniques","authors":"Xiao-Yi Zhang, Mingyue Jiang","doi":"10.1109/ICSME52107.2021.00039","DOIUrl":"https://doi.org/10.1109/ICSME52107.2021.00039","url":null,"abstract":"Spectrum-Based Fault Localisation (SBFL) is a well-known technique to find faulty statements in a program. To date, various techniques aiming to improve SBFL from different aspects have been proposed, following their own theories and assumptions. Therefore, it is challenging to make a fair assessment of their rationale and practicability. In this paper, we propose the SPectra Illustration for Comprehensive Analysis (SPICA), a methodology for reviewing and analysing existing SBFL works using spectrum visualisation. Specifically, taking as input a specific SBFL technique (e.g., a suspiciousness metric), SPICA illustrates the relevant artefacts within the spectrum space and then analyse the visualised spectra distribution following the steps of 1) examining the Geometric Characteristics (GCs) and 2) knowledge mining. In this way, we can do overall reviews for various SBFL techniques and get analysis results. As examples, we use SPICA to analyse five representative SBFL techniques, which provide fundamental theories or experimental results. Finally, we provide an overall assessment of the rationale for each technique, attached with suggestions that could be useful for future validation and extension.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130855318","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

Proceedings 2021 IEEE International Conference on Software Maintenance and Evolution 2021年IEEE软件维护与发展国际会议论文集

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.1109/icsme52107.2021.00001

引用次数: 2

BiasHeal: On-the-Fly Black-Box Healing of Bias in Sentiment Analysis Systems BiasHeal:情绪分析系统中偏见的即时黑盒治疗

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.26226/morressier.613b5419842293c031b5b648

Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, D. Lo

{"title":"BiasHeal: On-the-Fly Black-Box Healing of Bias in Sentiment Analysis Systems","authors":"Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, D. Lo","doi":"10.26226/morressier.613b5419842293c031b5b648","DOIUrl":"https://doi.org/10.26226/morressier.613b5419842293c031b5b648","url":null,"abstract":"Although Sentiment Analysis (SA) is widely applied in many domains, existing research has revealed that the unfairness in SA systems can be harmful to the welfare of less privileged people. Several works propose pre-processing and in-processing methods to eliminate bias in SA systems, but little attention is paid to utilizing post-processing methods to heal bias. Postprocessing methods are particularly important for systems that use third-party SA services. Systems that use such services have no access to the SA engine or its training data and thus cannot apply pre-processing nor in-processing methods. Therefore, this paper proposes a black-box post-processing method to make an SA system heal bias and construct fair results when bias is detected. We propose and investigate six self-healing strategies. Our evaluation results on two datasets show that the best strategy can construct fair results and improve accuracy on the two datasets by 2.76% and 2.85%, respectively. To the best of our knowledge, our work is the first self-healing method that can be deployed to ensure SA fairness without requiring access to the SA engine or its training data.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133494587","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

Migrating GUI behavior: from GWT to Angular 迁移GUI行为:从GWT到Angular

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.1109/ICSME52107.2021.00050

B. Verhaeghe, Anas Shatnawi, Abderrahmane Seriai, N. Anquetil, Anne Etien, Stéphane Ducasse, Mustapha Derras

{"title":"Migrating GUI behavior: from GWT to Angular","authors":"B. Verhaeghe, Anas Shatnawi, Abderrahmane Seriai, N. Anquetil, Anne Etien, Stéphane Ducasse, Mustapha Derras","doi":"10.1109/ICSME52107.2021.00050","DOIUrl":"https://doi.org/10.1109/ICSME52107.2021.00050","url":null,"abstract":"In a collaboration with Berger-Levrault, a major IT company, we are working on the migration of GWT applications to Angular. We focus on the GUI aspect of this migration which requires a framework switch (GWT to Angular) and a programming language switch (Java to TypeScript). Previous work identified that the GUI can be split into the UI structure and the GUI behavioral code. GUI behavioral code is the code executed when the user interacts with the UI. Although the migration of UI structure has already been studied, the migration of the GUI behavioral code has not. To help developers during the migration of their applications, we propose a generic approach in four steps that uses a meta-model to represent the GUI behavioral code. This approach includes a separation of the GUI behavioral code into events (caller code) and the code executed when an event is fired (called code). We present the approach and its implementation for a real industrial case study. The application comprises 470 Java (GWT) classes representing 56 web pages. We give examples of the migrated code. We evaluate the quality of the generated code with standard tools (SonarQube, codelizer) and compare it to another Java to TypeScript converter. The results show that our code has 53% fewer warnings and rule violations for SonarQube, and 99% fewer for codelizer.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133546323","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

The Unit Test Quality of Deep Learning Libraries: A Mutation Analysis 深度学习库的单元测试质量:一个突变分析

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.26226/morressier.613b5418842293c031b5b5cb

Li Jia, Hao Zhong, Linpeng Huang

{"title":"The Unit Test Quality of Deep Learning Libraries: A Mutation Analysis","authors":"Li Jia, Hao Zhong, Linpeng Huang","doi":"10.26226/morressier.613b5418842293c031b5b5cb","DOIUrl":"https://doi.org/10.26226/morressier.613b5418842293c031b5b5cb","url":null,"abstract":"In recent years, with the flourish of deep learning techniques, deep learning libraries have been used by many smart applications. As smart applications are used in critical scenarios, their bugs become a concern, and bugs in deep learning libraries have far-reaching impacts on their built-on applications. Although programmers write many test cases for deep learning libraries, to the best of our knowledge, no prior study has ever explored to what degree such test cases are sufficient. As a result, some fundamental questions about these test cases are still open. For example, to what degree can existing test cases detect bugs in deep libraries? How to improve such test cases? To help programmers improve their test cases and to shed light on the detection techniques of deep learning bugs, there is a strong need for a study on the test quality of deep learning libraries. To meet the strong need, in this paper, we conduct the first empirical study on this issue. Our basic idea is to inject bugs into deep learning libraries, and to check to what degree existing test cases can detect our injected bugs. With a mutation tool, we constructed 1,545 buggy versions (i.e., mutants). By comparing the testing results between clean and buggy versions, our study leads to 11 findings, and we summarize them into the answers to three research questions. For example, we find that although existing test cases detected 60% of our injected bugs, only 30% of such bugs were detected by the assertions of these test cases. As another example, we find that some exceptions were thrown only in specific learning phases. Furthermore, we interpret our results from the perspectives of researchers, library developers, and application programmers.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133553882","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: 8

Report From The Trenches A Case Study In Modernizing Software Development Practices 来自战壕的报告:现代化软件开发实践的案例研究

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.26226/morressier.613b5418842293c031b5b605

Hou'ekp'etodji Mahugnon Honor'e, N. Anquetil, Stéphane Ducasse, Fatiha Djareddir, Jerôme Sudich

引用次数: 0

Organizing Committee ICSME 2021 组委会ICSME 2021

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.1109/icsme52107.2021.00006

引用次数: 0

Efficient Platform Migration of a Mainframe Legacy System Using Custom Transpilation 使用自定义编译的大型机遗留系统的高效平台迁移

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.26226/morressier.613b5419842293c031b5b64a

Markus Schnappinger, Jonathan Streit

{"title":"Efficient Platform Migration of a Mainframe Legacy System Using Custom Transpilation","authors":"Markus Schnappinger, Jonathan Streit","doi":"10.26226/morressier.613b5419842293c031b5b64a","DOIUrl":"https://doi.org/10.26226/morressier.613b5419842293c031b5b64a","url":null,"abstract":"Legacy software systems play an important role in the economy but are known to cause high operational and maintenance costs. To reduce these costs, such systems are often migrated to modern infrastructure or languages. There exists a variety of migration strategies, however choosing the best strategy or combination of strategies given technical, economical and business constraints remains a challenging task. We observe a lack of experience reports on industrial migration projects explaining their decisions in detail. In this report, we present the case of an insurance system with 1M Source Lines of Code, running on an expensive mainframe and featuring Natural, Cobol, and Assembler code as well as an Adabas database. We elaborate on why state-of-practice migration strategies were inadequate in this case and introduce an alternative methodology, taking into account the limited budget for the migration. In this project, we use custom transpilation to translate the legacy code automatically to another programming language. In contrast to off-the-shelf transpilers, we implement an iteratively refined transpiler that is fine-tuned to the legacy code at hand. The transpiler guides its own development by pointing out instructions in the legacy code it cannot yet translate. Manual adaptions to the legacy code allow circumventing the implementation of overly complicated translation rules. This ensures the transpiler and the generated code remain lean and efficient while being able to cope with specific challenges of the system at hand. In the presented industrial case, Natural and Assembler sources were transpiled to Cobol running on Linux, combined with some adapted and rewritten Cobol and Java. We illustrate our lessons learned and provide in-depth insights into testing and debugging activities. A comparison with alternative offers by other vendors validates the economic benefits of this approach.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133902660","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

Assessing Generalizability of CodeBERT 评估CodeBERT的泛化性

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.26226/morressier.613b5418842293c031b5b630

Xin Zhou, Donggyun Han, David Lo

{"title":"Assessing Generalizability of CodeBERT","authors":"Xin Zhou, Donggyun Han, David Lo","doi":"10.26226/morressier.613b5418842293c031b5b630","DOIUrl":"https://doi.org/10.26226/morressier.613b5418842293c031b5b630","url":null,"abstract":"Pre-trained models like BERT have achieved strong improvements on many natural language processing (NLP) tasks, showing their great generalizability. The success of pre-trained models in NLP inspires pre-trained models for programming language. Recently, CodeBERT, a model for both natural language (NL) and programming language (PL), pre-trained on code search dataset, is proposed. Although promising, CodeBERT has not been evaluated beyond its pre-trained dataset for NL-PL tasks. Also, it has only been shown effective on two tasks that are close in nature to its pre-trained data. This raises two questions: Can CodeBERT generalize beyond its pre-trained data? Can it generalize to various software engineering tasks involving NL and PL? Our work answers these questions by performing an empirical investigation into the generalizability of CodeBERT. First, we assess the generalizability of CodeBERT to datasets other than its pre-training data. Specifically, considering the code search task, we conduct experiments on another dataset containing Python code snippets and their corresponding documentation. We also consider yet another dataset of questions and answers collected from Stack Overflow about Python programming. Second, to assess the generalizability of CodeBERT to various software engineering tasks, we apply CodeBERT to the just-in-time defect prediction task. Our empirical results support the generalizability of CodeBERT on the additional data and task. CodeBERT-based solutions can achieve higher or comparable performance than specialized solutions designed for the code search and just-in-time defect prediction tasks. However, the superior performance of the CodeBERT requires a tradeoff; for example, it requires much more computation resources as compared to specialized code search approaches.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130817244","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: 37

DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing DeepOrder:持续集成测试中测试用例优先级的深度学习

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2021-09-01 DOI: 10.26226/morressier.613b5418842293c031b5b5de

Aizaz Sharif, D. Marijan, Marius Liaaen

{"title":"DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing","authors":"Aizaz Sharif, D. Marijan, Marius Liaaen","doi":"10.26226/morressier.613b5418842293c031b5b5de","DOIUrl":"https://doi.org/10.26226/morressier.613b5418842293c031b5b5de","url":null,"abstract":"Continuous integration testing is an important step in the modern software engineering life cycle. Test prioritization is a method that can improve the efficiency of continuous integration testing by selecting test cases that can detect faults in the early stage of each cycle. As continuous integration testing produces voluminous test execution data, test history is a commonly used artifact in test prioritization. However, existing test prioritization techniques for continuous integration either cannot handle large test history or are optimized for using a limited number of historical test cycles. We show that such a limitation can decrease fault detection effectiveness of prioritized test suites. This work introduces DeepOrder, a deep learning-based model that works on the basis of regression machine learning. DeepOrder ranks test cases based on the historical record of test executions from any number of previous test cycles. DeepOrder learns failed test cases based on multiple factors including the duration and execution status of test cases. We experimentally show that deep neural networks, as a simple regression model, can be efficiently used for test case prioritization in continuous integration testing. DeepOrder is evaluated with respect to time-effectiveness and fault detection effectiveness in comparison with an industry practice and the state of the art approaches. The results show that DeepOrder outperforms the industry practice and state-of-the-art test prioritization approaches in terms of these two metrics.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122850319","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: 13