Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution最新文献

Toward static test flakiness prediction: a feasibility study 对静态试验薄片预测的可行性研究

Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution Pub Date : 2021-08-23 DOI: 10.1145/3472674.3473981

Valeria Pontillo, Fabio Palomba, F. Ferrucci

{"title":"Toward static test flakiness prediction: a feasibility study","authors":"Valeria Pontillo, Fabio Palomba, F. Ferrucci","doi":"10.1145/3472674.3473981","DOIUrl":"https://doi.org/10.1145/3472674.3473981","url":null,"abstract":"Flaky tests are tests that exhibit both a passing and failing behavior when run against the same code. While the research community has attempted to define automated approaches for detecting and addressing test flakiness, most of them suffer from scalability issues and uncertainty as they require test cases to be run multiple times. This limitation has been recently targeted by means of machine learning solutions that could predict the flakiness of tests using a set of both static and dynamic metrics that would avoid the re-execution of tests. Recognizing the effort spent so far, this paper poses the first steps toward an orthogonal view of the problem, namely the classification of flaky tests using only statically computable software metrics. We propose a feasibility study on 72 projects of the iDFlakies dataset, and investigate the differences between flaky and non-flaky tests in terms of 25 test and production code metrics and smells. First, we statistically assess those differences. Second, we build a logistic regression model to verify the extent to which the differences observed are still significant when the metrics are considered together. The results show a relation between test flakiness and a number of test and production code factors, indicating the possibility to build classification approaches that exploit those factors to predict test flakiness.","PeriodicalId":265246,"journal":{"name":"Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121057471","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

Building a bot for automatic expert retrieval on discord 建立一个机器人自动专家检索不和谐

Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution Pub Date : 2021-08-23 DOI: 10.1145/3472674.3473982

Ignacio Nuñez Norambuena, Alexandre Bergel

引用次数: 5

Metrics selection for load monitoring of service-oriented system 面向服务系统负载监控的指标选择

Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution Pub Date : 2021-08-23 DOI: 10.1145/3472674.3473983

Francesco Lomio, Sampsa Jurvansuu, D. Taibi

{"title":"Metrics selection for load monitoring of service-oriented system","authors":"Francesco Lomio, Sampsa Jurvansuu, D. Taibi","doi":"10.1145/3472674.3473983","DOIUrl":"https://doi.org/10.1145/3472674.3473983","url":null,"abstract":"Background. Complex software systems produce a large amount of data depicting their internal state and activities. The data can be monitored to make estimations and predictions of the status of the system, helping taking preventative actions in case of impending malfunctions and failures. However, a complex system may reveal thousands of internal metrics, which makes it a non-trivial task to decide which metrics are the most important to monitor. Objective. In this work we aim at finding a subset of metrics to collect and analyse for the monitoring of the load in a Service-oriented system. Method. We use a performance test bench tool to generate load of different intensities on the target system, which is a specific service-oriented application platform. The numeric metrics data collected from the system is combined with the load intensity at each moment. The combined data is used to analyse which metrics are best at estimating the load of the system. By using a regression analysis it was possible to rank the metrics by their ability to measure the load of the system. Results. The results show that (1) the use of machine learning regressor allows to correctly measure the load of a service-oriented system, and (2) the most important metrics are related to network traffic and request counts, as well as memory usage and disk activity. Conclusion. The results help with the designs of efficient monitoring tool. In addition, further investigation should be focused on exploring more precise machine learning model to further improve the metric selection process.","PeriodicalId":265246,"journal":{"name":"Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125050348","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

VaryMinions: leveraging RNNs to identify variants in event logs VaryMinions:利用rnn来识别事件日志中的变量

Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution Pub Date : 2021-08-23 DOI: 10.1145/3472674.3473980

Sophie Fortz, Paul Temple, Xavier Devroey, P. Heymans, Gilles Perrouin

引用次数: 4

Unsupervised learning of general-purpose embeddings for code changes 用于代码更改的通用嵌入的无监督学习

Proceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution Pub Date : 2021-06-03 DOI: 10.1145/3472674.3473979

Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev, T. Bryksin

引用次数: 1