2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)最新文献_第2页

The Phantom Menace: Unmasking Security Issues in Evolving Software 幽灵的威胁:揭露发展中的软件中的安全问题

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/icsme55016.2022.00085

Emanuele Iannone, Fabio Palomba

引用次数: 0

Defuse: A Data Annotator and Model Builder for Software Defect Prediction 化解:用于软件缺陷预测的数据注释器和模型构建器

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00063

Stefano Dalla Palma, D. D. Nucci, D. Tamburri

引用次数: 1

eTagger - An Energy Pattern Tagging Tool for GitHub Issues in Android Projects eTagger -一个用于Android项目中GitHub问题的能量模式标记工具

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00064

Shriram Shanbhag, S. Chimalakonda, V. Sharma, Vikrant S. Kaulgud

{"title":"eTagger - An Energy Pattern Tagging Tool for GitHub Issues in Android Projects","authors":"Shriram Shanbhag, S. Chimalakonda, V. Sharma, Vikrant S. Kaulgud","doi":"10.1109/ICSME55016.2022.00064","DOIUrl":"https://doi.org/10.1109/ICSME55016.2022.00064","url":null,"abstract":"Energy efficiency is an essential consideration in mobile application development, given that these apps run on battery-powered devices. This has led the researchers to develop a set of energy design patterns that can help the developers improve the energy efficiency of their applications. However, the adoption of these energy patterns in projects remains a challenge, given the lack of awareness about these patterns among the developers. To bridge this gap, we propose our tool eTagger, a Google Chrome extension that tags GitHub issues from Android repositories with associated energy patterns. eTagger works based on the embeddings generated by Sentence-BERT. We believe that labeling the GitHub issues with energy patterns may help towards their larger adoption as GitHub is a prominent platform in collaborative software development. A preliminary evaluation of eTagger achieved an AUC-ROC of 0.73 with a precision of 0.58, recall of 0.53 and an F1-score of 0.5. The demonstration of the tool is available at https://youtu.be/hP4pWJ4AKxE and related artifacts at https://rishalab.github.io/eTagger/.","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121604054","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

RestTestGen: An Extensible Framework for Automated Black-box Testing of RESTful APIs RestTestGen:一个用于RESTful api自动黑盒测试的可扩展框架

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00068

Davide Corradini, Amedeo Zampieri, Michele Pasqua, M. Ceccato

{"title":"RestTestGen: An Extensible Framework for Automated Black-box Testing of RESTful APIs","authors":"Davide Corradini, Amedeo Zampieri, Michele Pasqua, M. Ceccato","doi":"10.1109/ICSME55016.2022.00068","DOIUrl":"https://doi.org/10.1109/ICSME55016.2022.00068","url":null,"abstract":"Over the past few years, several novel black-box testing approaches targeting RESTful APIs have been proposed. In order to assess their effectiveness, such testing strategies had to be implemented as a prototype tool and validated on empirical data. However, developing a testing tool is a time-consuming task, and reimplementing from scratch the same common basic features represents a waste of resources that causes a remarkable overhead in the \"time to market\" of research results.In this paper, we present RestTestGen, an extensible framework for implementing new automated black-box testing strategies for RESTful APIs. The framework provides a collection of commonly used components, such as a robust OpenAPI specification parser, dictionaries, input value generators, mutation operators, oracles, and others. Many of the provided components are customizable and extensible, enabling researchers and practitioners to quickly prototype, deploy, and evaluate their novel ideas. Additionally, the framework facilitates the development of novel black-box testing strategies by guiding researchers, by means of abstract components that explicitly identify those parts of the framework requiring a concrete implementation.As an adoption example, we show how we can implement nominal and error black-box testing strategies for RESTful APIs, by reusing primitives and features provided by the framework, and by concretely extending very few abstract components.RestTestGen is open-source, actively maintained, and publicly available on GitHub at https://github.com/SeUniVr/RestTestGen","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114053888","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

Quantifying the Potential to Automate the Synchronization of Variants in Clone-and-Own 量化克隆与自有中自动同步变异的潜力

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00032

Alexander Schultheiss, P. M. Bittner, Thomas Thüm, Timo Kehrer

{"title":"Quantifying the Potential to Automate the Synchronization of Variants in Clone-and-Own","authors":"Alexander Schultheiss, P. M. Bittner, Thomas Thüm, Timo Kehrer","doi":"10.1109/ICSME55016.2022.00032","DOIUrl":"https://doi.org/10.1109/ICSME55016.2022.00032","url":null,"abstract":"In clone-and-own - the predominant paradigm for developing multi-variant software systems in practice - a new variant of a software system is created by copying and adapting an existing one. While clone-and-own is flexible, it causes high maintenance effort in the long run as cloned variants evolve in parallel; certain changes, such as bug fixes, need to be propagated between variants manually. On top of the principle of cherry-picking and by collecting lightweight domain knowledge on cloned variants and software changes, a recent line of research proposes to automate such synchronization tasks when migration to a software product line is not feasible. However, it is yet unclear how far this synchronization can actually be pushed. We conduct an empirical study in which we quantify the potential to automate the synchronization of variants in clone-and-own. We simulate the variant synchronization using the history of a real-world multi-variant software system as a case study. Our results indicate that existing patching techniques propagate changes with an accuracy of up to 85%, if applied consistently from the start of a project. This can be even further improved to 93% by exploiting lightweight domain knowledge about which features are affected by a change, and which variants implement affected features. Based on our findings, we conclude that there is potential to automate the synchronization of cloned variants through existing patching techniques.","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133885978","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

Evaluating Atoms of Confusion in the Context of Code Reviews 评估代码审查环境中的混乱原子

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00048

Victoria Bogachenkova, Linh Nguyen, Felipe Ebert, Alexander Serebrenik, Fernando Castor

{"title":"Evaluating Atoms of Confusion in the Context of Code Reviews","authors":"Victoria Bogachenkova, Linh Nguyen, Felipe Ebert, Alexander Serebrenik, Fernando Castor","doi":"10.1109/ICSME55016.2022.00048","DOIUrl":"https://doi.org/10.1109/ICSME55016.2022.00048","url":null,"abstract":"Code review is a popular software engineering practice. Success of code reviews can be threatened by confusion experienced by code reviewers. For instance, on the one hand, research has studied the reasons for confusion in code reviews, and on the other hand, it also has analyzed source code patterns, so called \"atoms of confusion\", that have been shown to lead to misunderstanding in the lab setting. However, to the best of our knowledge, there is no research which tried to investigate the possible cause and effect relationship between atoms of confusion and confusion in code reviews. Another important aspect still not studied is how those atoms of confusion evolve across pull requests. In this emerging results paper, we report an exploratory case study to provide a deeper understanding of atoms of confusion, more specifically, whether atoms of confusion are related to confusion in code reviews and how they persist across pull requests. With the help of an existing tool for the detection of atoms of confusion, and a manual analysis of code reviews comments, we observed that statistical analysis did not show any relationship between atoms of confusion and presence of confusion comments in code reviews. Additionally, we found evidence that atoms of confusion are mostly not being removed in pull requests. Based on the results, we formulate hypotheses on atoms of confusion in the code review context, that should be confirmed or rejected by future studies.","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130316903","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

Heterogeneous Vulnerability Report Traceability Recovery by Vulnerability Aspect Matching 基于漏洞方面匹配的异构漏洞报告可跟踪性恢复

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00024

Jiamou Sun, Zhenchang Xing, Xiwei Xu, Liming Zhu, Qinghua Lu

{"title":"Heterogeneous Vulnerability Report Traceability Recovery by Vulnerability Aspect Matching","authors":"Jiamou Sun, Zhenchang Xing, Xiwei Xu, Liming Zhu, Qinghua Lu","doi":"10.1109/ICSME55016.2022.00024","DOIUrl":"https://doi.org/10.1109/ICSME55016.2022.00024","url":null,"abstract":"Security databases describe characteristics of discovered vulnerabilities in text for future studying and patching. However, due to different maintainers having different perspectives about vulnerabilities, they often describe the same vulnerability in different ways, creating obstacles for gathering comprehensive information about the vulnerabilities from different databases. To mitigate this problem, Common Vulnerability and Exposures (CVE) is established to identify each publicly disclosed vulnerability with a unique CVE id, and vulnerability databases by different vendors and organizations can reference the CVE ids in their vulnerability reports. In spite of the wide adoption of CVEs, traceability issues are still prevalent. Our empirical study on vulnerability traceability across four representative security databases (NVD, IBM X-Force, ExploitDB, Openwall) shows that there was a fast-increasing amount of CVE records, traceability delay, and missing issues become severe for the vulnerability databases. To address these issues, we develop an automatic traceability recovery method for recommending related external vulnerability reports to the reports in one database. As vulnerability reports from different databases differ in content details and length, our approach does not match the reports at the document level but extracts seven distinctive vulnerability key aspects that are widely present in vulnerability descriptions. As a proof of concept, we apply our methods to recommend the reports from IBM X-Force, ExploitDB and Openwall to the NVD report. We use NVD as the target because it is a de-facto standard vulnerability database that contains the most comprehensive list of vulnerabilities. Our experiments on a wide range of NLP methods show our aspect-level matching methods can achieve high MRR and accuracy for traceability recovery across heterogeneous vulnerability databases.","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115204236","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

Learning-based Identification of Coding Best Practices from Software Documentation 基于学习的软件文档编码最佳实践识别

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00073

Neela Sawant, Srinivasan H. Sengamedu

{"title":"Learning-based Identification of Coding Best Practices from Software Documentation","authors":"Neela Sawant, Srinivasan H. Sengamedu","doi":"10.1109/ICSME55016.2022.00073","DOIUrl":"https://doi.org/10.1109/ICSME55016.2022.00073","url":null,"abstract":"Automatic identification of coding best practices can scale the development of code and application analyzers. We present Doc2BP, a deep learning tool to identify coding best practices in software documentation. Natural language descriptions are mapped to an informative embedding space, optimized under the dual objectives of binary and few shot classification. The binary objective powers general classification into known best practice categories using a deep learning classifier. The few shot objective facilitates example-based classification into novel categories by matching embeddings with user-provided examples at run-time, without having to retrain the underlying model. We analyze the effects of manually and synthetically labeled examples, context, and cross-domain information.We have applied Doc2BP to Java, Python, AWS Java SDK, and AWS CloudFormation documentations. With respect to prior works that primarily leverage keyword heuristics and our own parts of speech pattern baselines, we obtain 3-5% F1 score improvement for Java and Python, and 15-20% for AWS Java SDK and AWS CloudFormation. Experiments with four few shot use-cases show promising results (5-shot accuracy of 99%+ for Java NullPointerException and AWS Java metrics, 65% for AWS CloudFormation numerics, and 35% for Python best practices).Doc2BP has contributed new rules and improved specifications in Amazon's code and application analyzers: (a) 500+ new checks in cfn-lint, an open-source AWS CloudFormation linter, (b) over 97% automated coverage of metrics APIs and related practices in Amazon DevOps Guru, (c) support for nullable AWS APIs in Amazon CodeGuru's Java NullPointerException (NPE) detector, (d) 200+ new best practices for Java, Python, and respective AWS SDKs in Amazon CodeGuru, and (e) 2% reduction in false positives in Amazon CodeGuru's Java resource leak detector.","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122727801","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

AIP: Scalable and Reproducible Execution Traces in Energy Studies on Mobile Devices AIP:移动设备能源研究中可扩展和可复制的执行轨迹

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00057

Olivier Nourry, Yutaro Kashiwa, B. Lin, G. Bavota, Michele Lanza, Yasutaka Kamei

{"title":"AIP: Scalable and Reproducible Execution Traces in Energy Studies on Mobile Devices","authors":"Olivier Nourry, Yutaro Kashiwa, B. Lin, G. Bavota, Michele Lanza, Yasutaka Kamei","doi":"10.1109/ICSME55016.2022.00057","DOIUrl":"https://doi.org/10.1109/ICSME55016.2022.00057","url":null,"abstract":"Energy consumption in mobile applications is a key area of software engineering studies, since any advance could affect billions of devices. Currently, several software-based energy calculation tools can provide close estimates of the energy consumed by mobile applications without relying on physical hardware, offering new opportunities to conduct large-scale energy studies in mobile devices. In these studies, one key step of data collection is generating events, since it allows exercising specific parts of the code and, as a consequence, assessing their energy consumption. Given the fact that manually generating events by interacting with applications is time-consuming and not scalable, large-scale studies often use software-based tools to automate event generation to profile devices. Existing tools rely on randomly generated events, which undermines the reproducibility and generalizability of such studies.We present AIP (Android Instrumentation Profiler), an alternative to existing software-based event generation tools such as Monkey. AIP uses instrumented tests as a source of event generation, which enables the targeting of complex use cases for energy consumption estimations, as well as the creation of fully reproducible events and execution traces, while maintaining the scaling abilities of other state-of-the-art tools. The tool and demo video can be found on https://github.com/ONourry/AndroidInstrumentationProfiler.","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131116375","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

VSCode Migrate: Semi-Automatic Migrations for Low Coverage Projects VSCode Migrate:低覆盖率项目的半自动迁移

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2022-10-01 DOI: 10.1109/ICSME55016.2022.00070

Tim Vahlbrock, Martin Guddat, Tom Vierjahn

{"title":"VSCode Migrate: Semi-Automatic Migrations for Low Coverage Projects","authors":"Tim Vahlbrock, Martin Guddat, Tom Vierjahn","doi":"10.1109/ICSME55016.2022.00070","DOIUrl":"https://doi.org/10.1109/ICSME55016.2022.00070","url":null,"abstract":"Modern software is subject to continuous change and so are its interfaces to other software. Introducing breaking changes to an interface requires its consumers to make adaptions to their own code base in order to compensate. Oftentimes, the number of changes requires a large effort when performed manually. Additionally, the places that require changes may be hard to find using simple pattern matching. Both have lead to the development of tools like ClangMR, which automatically find and adapt affected pieces of code. Such automatic tools, however, assume that the correctness of the applied changes will be verified by tests. This makes them risky to use for projects with a low test coverage.In this paper we present VSCode Migrate, an IDE extension to perform semi-automatic migrations, enabling large refactorings in low coverage projects. The locations that need to be refactored can be found using alternative matching strategies, including AST based ones, and the changes to perform can be generated automatically. However, instead of applying the changes immediately, VSCode Migrate lets the developer verify and modify each adaption. If a change is not sufficiently covered, additional tests can be added before the change is applied. These mechanisms provide the safeguarding necessary for projects with low test coverage.","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116915940","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