An extensive study of class-level and method-level test case selection for continuous integration

Li, Yingling; Wang, Junjie; Yang, Yun; Wang, Qing

doi:10.1016/j.jss.2020.110614

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…Gambar 2 memperlihatkan sebaran artikel hasil seleksi yang dikelompokkan per tahun. (Li et al, 2020) 12 aplikasi open source dari eclipse community: jdt core, jdt debug, jdt ui, pde ui, platform debug, platform resources, platform runtime, platform team, platform text, platform ua, dan platform ui, dan equinox framework. Serta menggunakan 6 aplikasi open source dar apache community: Apache OOZIE, Apache ratis, Apache Streams, Beam, Apache Hadoop, dan Apache HBase Aplikasi Open Source OpenMRS, NumbertoWord, dan Palindrom (Butool et al, 2019) Aplikasi Open-Source ATM, Online Food Ordering, dan Process Flow Manager (Hettiarachchi and Do, 2019) Aplikasi iTrust versi 0, 1, 2, dan 3 (Pradhan et al, 2019) Dua versi produk VCS dari Cisco, serta aplikasi Paint Control dan IOF/ROL dari ABB Robotic (Yadav and Dutta, 2020) Aplikasi Open-Source ATM (Someoliayi et al, 2019) Aplikasi Apache Commons Math dan Apache Commons Lang (Tran et al, 2020) Aplikasi Open Source BugZilla, Launchpad, Mantis, dan Debian (Azizi and Do, 2018b) Aplikasi Open Source nopCommerce, Umbraco-CMS, jmeter, jtopas (Hilton et al, 2018) 47 Proyek Open-Source yang dibangun dengan Bahasa Java, Python, Elixer, Scala, Golang, Node.js, dan DotNet (Marcozzi et al, 2017) 14 Aplikasi Open Source menggunakan Bahasa C: 7 dari program Siemens, 4 dari cryptographic OpenSSL toolkit, 1 GNU Zip Compression Program, 1 aplikasi Sjeng Chess, dan SQLite relational database management system (Akour et al, 2018) Aplikasi Open-Source Cinema (Öztürk, 2017) 5 aplikasi open source: Datastructures-csharp, Epicylce.math, GimSharp, ProjectEuler, dan YAMP (Singh and Srivastava, 2018) Aplikasi Open-Source AspectJ dan HealthWatcher…”

Section: Pencarian Artikelunclassified

Software Under Test Dalam Penelitian Software Testing: Sebuah Review

Rahmani

Min

Suprihanto

2021

JTT

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Software under Test (SUT) is an essential aspect of software testing research activities. Preparation of the SUT is not simple. It requires accuracy, completeness and will affect the quality of the research conducted. Currently, there are several ways to utilize an SUT in software testing research: building an own SUT, utilization of open source to build an SUT, and SUT from the repository utilization. This article discusses the results of SUT identification in many software testing studies. The research is conducted in a systematic literature review (SLR) using the Kitchenham protocol. The review process is carried out on 86 articles published in 2017-2020. The article was selected after two selection stages: the Inclusion and Exclusion Criteria and the quality assessment. The study results show that the trend of using open source is very dominant. Some researchers use open source as the basis for developing SUT, while others use SUT from a repository that provides ready-to-use SUT. In this context, utilization of the SUT from the software infrastructure repository (SIR) and Defect4J are the most significant choice of researchers.

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Section: Pencarian Artikelunclassified

Software Under Test Dalam Penelitian Software Testing: Sebuah Review

Rahmani

Min

Suprihanto

2021

JTT

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“…Test case prioritization is often related to code. Many methods consider code coverage [8], defect detection rate [9], requirement correlation [10], test case similarity [11], test history [12] and other information when ranking test cases [13]. In recent years, machine-learning-based and search-based test case sorting methods have become mainstream [14], however, the data used for training in most methods still needs to be extracted from system code.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Test Case Prioritization in Continuous Integration based on Reinforcement Learning

Han,

Chen,

Han

2023

Atlantis Highlights in Engineering

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The iterative update of software leads to frequent continuous integration, so the testing in the continuous integration environment should also be fast and accurate. Reinforcement learning is often used in the research of continuous integration testing because of its sequential strategy and good robustness. Some existing methods use reinforcement learning to solve test case prioritization problem, which provides a good idea, but the experimental defect detection rates are relatively low. Therefore, based on the existing reinforcement learning framework, this article proposes a reward mechanism to provide additional rewards for newly emerging test cases in each integration cycle. Through experiments on three industrial datasets, it has been proven that this mechanism improves the defect detection rate, the recall rate of failed test cases, and the efficiency of test feedback in the testing process.

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