Applying Incremental Model Slicing to Product-Line Regression Testing

2016 ◽  
pp. 3-19 ◽  
Author(s):  
Sascha Lity ◽  
Thomas Morbach ◽  
Thomas Thüm ◽  
Ina Schaefer
Keyword(s):  
Product Line ◽  
Regression Testing ◽  
Incremental Model

Retest test selection for product-line regression testing of variants and versions of variants

2019 ◽  
Vol 147 ◽  
pp. 46-63 ◽  
Author(s):  
Sascha Lity ◽  
Manuel Nieke ◽  
Thomas Thüm ◽  
Ina Schaefer
Keyword(s):  
Product Line ◽  
Regression Testing ◽  
Test Selection ◽  
Selection For

scholarly journals Efficient Regression Testing of Software Product Lines by Reducing Redundant Test Executions

2020 ◽  
Vol 10 (23) ◽  
pp. 8686
Author(s):  
Pilsu Jung ◽  
Sungwon Kang ◽  
Jihyun Lee
Keyword(s):  
Product Family ◽  
Software Product Lines ◽  
Product Line ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Product Lines ◽  
Test Selection ◽  
Software Product ◽  
Regression Test

Regression testing for software product lines (SPLs) is challenging because it must ensure that all the products of a product family work correctly whenever changes are made. One approach to reducing the cost of regression testing is the regression test selection (RTS), which selects a subset of regression test cases. However, even when RTS is applied, SPL regression testing can still be expensive because, in the product line context, each test case can be executed on more than one product that reuses the test case, which would typically result in a large number of test executions. A promising direction is to eliminate redundant test executions of test cases. We propose a method that, given a test case, identifies a set of products, on which the test case will cover the same sequence of source code statements and produce the same testing results, and then excludes these products from products to apply the test case to. The evaluation results showed that when the full selection approach and the approach of repetitively applying an RTS method for a single software system are used for test selection, our method reduced, respectively, 59.3% and 40.0% of the numbers of test executions of the approaches.

scholarly journals Using Global Constraints to Automate Regression Testing

AI Magazine ◽  
2017 ◽  
Vol 38 (1) ◽  
pp. 73-87
Author(s):  
Arnaud Gotlieb ◽  
Dusica Marijan
Keyword(s):  
Product Line ◽  
Regression Testing ◽  
Test Suite ◽  
Global Constraints ◽  
Test Case ◽  
Delivery Mode ◽  
Test Cases ◽  
High Quality ◽  
Test Suite Reduction ◽  
Test Requirements

Nowadays, any communicating or autonomous systems rely on high-quality software-based components. To ensure a sufficient level of quality, these components must be thoroughly verified before being released and being deployed in operational settings. Regression testing is a crucial verification process that executes any new release of a software-based component against previous versions of the component, with existing test cases. However, the selection of test cases in regression testing is challenging as the time available for testing is limited and some selection criteria must be respected. This problem, coined as Test Suite Reduction (TSR), is usually addressed by validation engineers through manual analysis or by using approximation techniques. Even if the underlying optimization problem is untractable in theory, solving it in practice is crucial when there are pressing needs to release high-quality components while at the same time reducing the time-to-market of new software releases. In this paper, we address the TSR problem with sound Artificial intelligence techniques such as Constraint Programming (CP) and global constraints. By associating each test case a cost-value aggregating distinct criteria, such as execution time, priority or importance due to the error-proneness of each test case, we propose several constraint optimization models to find a subset of test cases covering all the test requirements and optimizing the overall cost of selected test cases. Our models are based on a combination of NVALUE, GLOBALCARDINALITY, and SCALAR_PRODUCT, three well-known global constraints that can faithfully encode the coverage relation between test cases and test requirements. Our contribution includes the reuse of existing preprocessing rules to simplify the problem before solving it and the design of structure-aware heuristics, which take into account the notion of costs, associated with test cases. The work presented in this paper has been motivated by an industrial application in the communication domain. Our overall goal is to develop a constraint-based approach of test suite reduction that can be deployed to test a complete product line of conferencing systems in continuous delivery mode. By implementing this approach in a software prototype tool and experimentally evaluated it on both randomly generated instances and industrial instances, we hope to foster a quick adoption of the technology.

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