Practical selective regression testing with effective redundancy in interleaved tests

Marijan, Dusica; Liaaen, Marius

doi:10.1145/3183519.3183532

Cited by 16 publications

(15 citation statements)

References 26 publications

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“…For instance, in C1, the testing of high-end video conferencing systems developed by Cisco Systems with several variable parameters and requirements for high video throughput pushed software testing research at SRL to handle a very large combinatorial problem. This led to novel algorithms published as scientific articles [35,[38][39][40][41]43] and software. In C2, SZ's Flow sensor was used as a wearable in health (obstructive sleep apnea), sports (force in a rowing oar), and music (breathing rhythms while performing).…”

Section: P5: Power Of Synergymentioning

confidence: 99%

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Marijan

Sen

2022

ACM Trans. Softw. Eng. Methodol.

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Increasing the impact of software engineering research in the software industry and the society at large has long been a concern of high priority for the software engineering community. The problem of two cultures, research conducted in a vacuum (disconnected from the real world), or misaligned time horizons are just some of the many complex challenges standing in the way of successful industry–academia collaborations. This article reports on the experience of research collaboration and knowledge co-creation between industry and academia in software engineering as a way to bridge the research–practice collaboration gap. Our experience spans 14 years of collaboration between researchers in software engineering and the European and Norwegian software and IT industry. Using the participant observation and interview methods, we have collected and afterwards analyzed an extensive record of qualitative data. Drawing upon the findings made and the experience gained, we provide a set of 14 patterns and 14 anti-patterns for industry–academia collaborations, aimed to support other researchers and practitioners in establishing and running research collaboration projects in software engineering.

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Section: P5: Power Of Synergymentioning

confidence: 99%

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Marijan

Sen

2022

ACM Trans. Softw. Eng. Methodol.

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“…And used an industrial case study for evaluation for continuous integration. Consequently, in [29] study authors presented selective regression testing remover inter learned test case redundancy of highly configurable system for continuous integration. They used averaged based redundancy metric and historical information of integration tests for removal of redundancy.…”

Section: Related Workmentioning

confidence: 99%

“…For verification of component-based system (C.B.S. ), a regression testing (R.T.) approach is used [29]- [32]. Its objective is to decrease testing effort, for reuse, execute, and prioritize test cases [33]- [37].…”

Section: Introductionmentioning

confidence: 99%

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Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

et al. 2020

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To survive in the competitive environment, most organizations have adopted componentbased software development strategies in the rapid technology advancement era and the proper utilization of cloud-based services. To facilitate the continuous configuration, reduce complexity, and faster system delivery for higher user satisfaction in dynamic scenarios. In cloud services, customers select services from web applications dynamically. Healthcare body sensors are commonly used for diagnosis and monitoring patients continuously for their emergency treatment. The healthcare devices are connected with mobile or laptop etc. on cloud environment with network and frequently change applications. Thus, organizations rely on regression testing during changes and implementation to validate the quality and reliability of the system after the alteration. However, for a large application with limited resources and frequently change component management activities in the cloud computing environment, component-based system verification is difficult and challenging due to irrelevant and redundant test cases and faults. In this study, proposed a test case selection and prioritization framework using a design pattern to increase the faults detection rate. First, we select test cases on frequently accessed components using observer patterns and, secondly, prioritize test cases on adopting some strategies. The proposed framework was validated by an experiment and compared with other techniques (previous faults based and random priority). Hence, experimental results show that the proposed framework successfully verified changes. Subsequently, the proposed framework increases the fault detection rate (i.e., more than 90%) than previous faults based and random priority (i.e., more than 80% respectively).

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“…Unlike these approaches, we focus on reducing the redundancy of highly interleaved regression tests for HCS in CI. In our previous work, we proposed an algorithm for minimizing the test redundancy of interleaved tests in HCS using feature overlap across a test suite and historical fault‐detection effectiveness of test cases. This algorithm helped classify regression test cases as unique, totally redundant, and partially redundant tests.…”

Section: Introductionmentioning

confidence: 99%

A learning algorithm for optimizing continuous integration development and testing practice

2018

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Summary Continuous integration, at its core, includes a set of practices that aim to prevent and reduce the cost of software integration issues by merging working software copies often. Regression testing is considered a good practice in software development with continuous integration, which ensures that code changes are not negatively affecting software functionality. As, nowadays, software development is carried out iteratively, with small code increments continuously developed and regression tested, it is of critical importance that continuous regression testing is time efficient. However, in practice, regression testing is often long lasting and faces scalability problems as software grows larger or as software changes are made more frequently. One contributing factor to these issues is test redundancy, which causes the same software functionality being tested multiple times across a test suite. In large‐scale software, especially highly configurable software, redundancy in continuous regression testing can significantly grow the size of test suites and negatively affect the cost effectiveness of continuous integration. This paper presents a practical learning algorithm for optimizing continuous integration testing by reducing ineffective test redundancy in regression suites. The novelty of the algorithm lies in learning and predicting the fault‐detection effectiveness of continuous integration tests using historical test records and combining this information with coverage‐based redundancy metrics. The goal is to identify ineffective redundancy, which is maximally reduced in the resulting regression test suite, thus reducing test time and improving the performance of continuous integration. We apply and evaluate the algorithm in two industrial projects of continuous integration. The results show that the proposed algorithm can improve the efficiency of continuous integration practice in terms of decreasing test execution time by 38% on average compared to the industry practice of our case study and by 40% on average compared to the retest‐all approach. The results further demonstrate no significant reduction in fault‐detection effectiveness of continuous regression testing. This suggests that the proposed algorithm contributes to the state of the practice in the continuous integration development and testing of highly configurable systems.

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Practical selective regression testing with effective redundancy in interleaved tests

Cited by 16 publications

References 26 publications

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

A learning algorithm for optimizing continuous integration development and testing practice

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