Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops最新文献_第3页

Generating API Test Data Using Deep Reinforcement Learning 使用深度强化学习生成API测试数据

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392214

Steyn Huurman, Xiaoying Bai, Thomas Hirtz

引用次数: 5

Cross-distribution Feedback in Software Ecosystems 软件生态系统中的交叉反馈

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392188

A. Foundjem

引用次数: 1

Deep Learning for Software Defect Prediction: A Survey 深度学习用于软件缺陷预测:综述

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391463

Safa Omri, C. Sinz

{"title":"Deep Learning for Software Defect Prediction: A Survey","authors":"Safa Omri, C. Sinz","doi":"10.1145/3387940.3391463","DOIUrl":"https://doi.org/10.1145/3387940.3391463","url":null,"abstract":"Software fault prediction is an important and beneficial practice for improving software quality and reliability. The ability to predict which components in a large software system are most likely to contain the largest numbers of faults in the next release helps to better manage projects, including early estimation of possible release delays, and affordably guide corrective actions to improve the quality of the software. However, developing robust fault prediction models is a challenging task and many techniques have been proposed in the literature. Traditional software fault prediction studies mainly focus on manually designing features (e.g. complexity metrics), which are input into machine learning classifiers to identify defective code. However, these features often fail to capture the semantic and structural information of programs. Such information is needed for building accurate fault prediction models. In this survey, we discuss various approaches in fault prediction, also explaining how in recent studies deep learning algorithms for fault prediction help to bridge the gap between programs' semantics and fault prediction features and make accurate predictions.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115841855","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: 32

Flexible Probabilistic Modeling for Search Based Test Data Generation 基于搜索的测试数据生成的灵活概率建模

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392215

R. Feldt, S. Yoo

{"title":"Flexible Probabilistic Modeling for Search Based Test Data Generation","authors":"R. Feldt, S. Yoo","doi":"10.1145/3387940.3392215","DOIUrl":"https://doi.org/10.1145/3387940.3392215","url":null,"abstract":"While Search-Based Software Testing (SBST) has improved significantly in the last decade we propose that more flexible, probabilistic models can be leveraged to improve it further. Rather than searching for an individual, or even sets of, test case(s) or datum(s) that fulfil specific needs the goal can be to learn a generative model tuned to output a useful family of values. Such generative models can naturally be decomposed into a structured generator and a probabilistic model that determines how to make non-deterministic choices during generation. While the former constrains the generation process to produce valid values the latter allows learning and tuning to specific goals. SBST techniques differ in their level of integration of the two but, regardless of how close it is, we argue that the flexibility and power of the probabilistic model will be a main determinant of success. In this short paper, we present how some existing SBST techniques can be viewed from this perspective and then propose additional techniques for flexible generative modelling the community should consider. In particular, Probabilistic Programming languages (PPLs) and Genetic Programming (GP) should be investigated since they allow for very flexible probabilistic modelling. Benefits could range from utilising the multiple program executions that SBST techniques typically require to allowing the encoding of high-level test strategies.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115284707","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: 3

Double Cycle Hybrid Testing of Hybrid Distributed IoT System 混合分布式物联网系统的双周期混合测试

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392218

Cyrine Zid, D. Humeniuk, Foutse Khomh, G. Antoniol

{"title":"Double Cycle Hybrid Testing of Hybrid Distributed IoT System","authors":"Cyrine Zid, D. Humeniuk, Foutse Khomh, G. Antoniol","doi":"10.1145/3387940.3392218","DOIUrl":"https://doi.org/10.1145/3387940.3392218","url":null,"abstract":"Testing heterogeneous IoT applications such as a home automation systems integrating a variety of devices poses serious challenges. Oftentimes requirements are vaguely defined. Consumer grade cyber-physical devices and software may not meet the reliability and quality standard needed. Plus, system behavior may partially depend on various environmental conditions. For example, WI-FI congestion may cause packet delay; meanwhile cold weather may cause an unexpected drop of inside temperature. We surmise that generating and executing failure exposing scenarios is especially challenging. Modeling phenomenons such as network traffic or weather conditions is complex. One possible solution is to rely on machine learning models approximating the reality. These models, integrated in a system model, can be used to define surrogate models and fitness functions to steer the search in the direction of failure inducing scenarios. However, these models also should be validated. Therefore, there should be a double loop co-evolution between machine learned surrogate models functions and fitness functions. Overall, we argue that in such complex cyber-physical systems, co-evolution and multi-hybrid approaches are needed.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121914500","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

Splicing Community Patterns and Smells: A Preliminary Study 剪接群落模式与气味:初步研究

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392204

M. D. Stefano, Fabiano Pecorelli, D. Tamburri, Fabio Palomba, A. D. Lucia

{"title":"Splicing Community Patterns and Smells: A Preliminary Study","authors":"M. D. Stefano, Fabiano Pecorelli, D. Tamburri, Fabio Palomba, A. D. Lucia","doi":"10.1145/3387940.3392204","DOIUrl":"https://doi.org/10.1145/3387940.3392204","url":null,"abstract":"Software engineering projects are now more than ever a community effort. In the recent past, researchers have shown that their success may not only depend on source code quality, but also on other aspects like the balance of distance, culture, global engineering practices, and more. In such a scenario, understanding the characteristics of the community around a project and foresee possible problems may be the key to develop successful systems. In this paper, we focus on this research problem and propose an exploratory study on the relation between community patterns, i.e., recurrent mixes of organizational or social structure types, and smells, i.e., sub-optimal patterns across the organizational structure of a software development community that may be precursors of some sort of social debt. We exploit association rule mining to discover frequent relations between them. Our findings show that different organizational patterns are connected to different forms of socio-technical problems, possibly suggesting that practitioners should put in place specific preventive actions aimed at avoiding the emergence of community smells depending on the organization of the project.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122109093","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: 16

Stack-Based Genetic Improvement 基于堆栈的遗传改良

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392174

Aymeric Blot, J. Petke

引用次数: 1

Society-Level Software Governance: A Challenging Scenario 社会级软件治理:一个具有挑战性的场景

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3392269

Jürgen Musil, Angelika Musil, Danny Weyns, S. Biffl

引用次数: 0

MSABot

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391501

Chun-Ting Lin, Shang-Pin Ma, Yu-Wen Huang

引用次数: 7

Domain-Based Fuzzing for Supervised Learning of Anomaly Detection in Cyber-Physical Systems 基于域的模糊技术在网络物理系统异常检测中的监督学习

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops Pub Date : 2020-06-27 DOI: 10.1145/3387940.3391486

Herman Wijaya, M. Aniche, A. Mathur

引用次数: 10