System-Level Test Case Prioritization Using Machine Learning

Lachmann, Remo; Schulze, Sandro; Nieke, Manuel; Seidl, Christoph; Schaefer, Ina

doi:10.1109/icmla.2016.0065

Cited by 61 publications

(64 citation statements)

References 20 publications

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“…Test case prioritization techniques schedule test cases in an order that increases their effectiveness in meeting some performance goals (e.g., rate of fault detection and number of test cases required to discover all the faults) [51,91,115]. They mostly use information about previous executions of test cases (e.g., [29,33,44,60,63,91]), human knowledge (e.g., [8,54,96,97,99,103]), or a model of the system under test (e.g., [34,53,55,101]). For instance, Shrikanth et al [98] propose a test case prioritization approach that takes into consideration customer-assigned priorities of requirements, developer-perceived implementation complexity, requirements volatility, and fault proneness of requirements.…”

Section: Test Case Prioritizationmentioning

confidence: 99%

“…Tonella et al [103] propose a test case prioritization technique using user knowledge through a machine learning algorithm (i.e., Case-Based Ranking). Lachmann et al [60] propose another test case prioritization technique for system-level regression testing based on supervised machine learning. They consider test case history and natural language test case descriptions for prioritization.…”

Section: Test Case Prioritizationmentioning

confidence: 99%

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Automating system test case classification and prioritization for use case-driven testing in product lines

et al. 2020

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Product Line Engineering (PLE) is a crucial practice in many software development environments where software systems are complex and developed for multiple customers with varying needs. At the same time, many development processes are use case-driven and this strongly influences their requirements engineering and system testing practices. In this paper, we propose, apply, and assess an automated system test case classification and prioritization approach specifically targeting system testing in the context of use case-driven development of product families. Our approach provides: (i) automated support to classify, for a new product in a product family, relevant and valid system test cases associated with previous products, and (ii) automated prioritization of system test cases using multiple risk factors such as fault-proneness of requirements and requirements volatility in a product family. Our evaluation was performed in the context of an industrial product family in the automotive domain. Results provide empirical evidence that we propose a practical and beneficial way to classify and prioritize system test cases for industrial product lines.

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Section: Test Case Prioritizationmentioning

confidence: 99%

Section: Test Case Prioritizationmentioning

confidence: 99%

Automating system test case classification and prioritization for use case-driven testing in product lines

et al. 2020

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“…Testing is a field that has advanced in the last few years. Several of these advances employ the AI techniques to prioritize the test cases [10] and automatize the test oracle [11], among others. Our approach, TDA, is focused on the anonimization of the data, keeping them useful for the AI.…”

Section: Related Work Aimentioning

confidence: 99%

Test-Driven Anonymization for Artificial Intelligence

Augusto

Morán

Riva

et al. 2019

2019 IEEE International Conference on Artificial Intelligence Testing (AITest)

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In recent years, data published and shared with third parties to develop artificial intelligence (AI) tools and services has significantly increased. When there are regulatory or internal requirements regarding privacy of data, anonymization techniques are used to maintain privacy by transforming the data. The side-effect is that the anonymization may lead to useless data to train and test the AI because it is highly dependent on the quality of the data. To overcome this problem, we propose a test-driven anonymization approach for artificial intelligence tools. The approach tests different anonymization efforts to achieve a trade-off in terms of privacy (non-functional quality) and functional suitability of the artificial intelligence technique (functional quality). The approach has been validated by means of two real-life datasets in the domains of healthcare and health insurance. Each of these datasets is anonymized with several privacy protections and then used to train classification AIs. The results show how we can anonymize the data to achieve an adequate functional suitability in the AI context while maintaining the privacy of the anonymized data as high as possible.

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“…Acknowledging and accepting the importance of test case design, finding a solution for the drawbacks in the design process is extremely crucial. It is well known fact that any manual repetitive job is inefficient, unreliable, tedious and time [1] In order to solve this problem we focused on automating test case designing process. We devised and tested our design engine which is based on parser and machine learning [6], [17] logic to automatically prioritize, categorize and generate test cases for non-complex User Interface (UI) elements of mobile and web applications.…”

Section: Introductionmentioning

confidence: 99%

Test Case Design and Test Case Prioritization using Machine Learning

Sharna¹,

Agrawal²,

Kumar³

2019

IJEAT

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Designing and prioritizing test cases is a very tedious task. Given all the advancements in the world of software testing, on any given day engineers spend several man-hours to identify all possible testing scenarios and preconditions attached with it. Test engineers then use the scenarios and preconditions to write multiple test cases. Every test case has a template skeleton to follow - expected results, actual results, priority, test suite category classification (regression, sanity, smoke, integration, etc.), and the respective software (i.e. version, build, release etc.) that has to be tested. Until now there have been efforts to make test case designing simpler by providing software test engineers with tools and processes. But these tools and processes still needs considerable amount of manual intervention in terms of understanding the requirements, analyzing the quality risks and documentation of all possible test scenarios in order to ensure a high quality software delivery. Man-hours spent on test case design and test case prioritization is directly proportional to the cost involved in building software. Our goal was to make sure that the manual intervention in test case design and test case prioritization is reduced to minimum without imposing any software quality risks. So, that the cost to ship and build software is reduced. With this paper we are presenting a solution to this problem. Our goal here was to use machine learning [5] to do automated test case prioritization and creation of test cases for software. In order to achieve this goal we used supervised machine learning [6] approach based on K-Nearest Neighbor classification model for test case design and test case prioritization [4]. On experimenting with other linear and non-linear classifiers we learnt that they did not prove to be as accurate as K-Nearest Neighbor. Our method of machine learning based automated test case design and test case prioritization can be used by any software development organization to reduce it’s software development cost and time taken to ship software to their respective consumers. This aims to benefit the software development industry as a whole.

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System-Level Test Case Prioritization Using Machine Learning

Cited by 61 publications

References 20 publications

Automating system test case classification and prioritization for use case-driven testing in product lines

Automating system test case classification and prioritization for use case-driven testing in product lines

Test-Driven Anonymization for Artificial Intelligence

Test Case Design and Test Case Prioritization using Machine Learning

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