Authors:
Arnaud Gotlieb
1
;
Mats Carlsson
2
;
Dusica Marijan
1
and
Alexandre Pétillon
1
Affiliations:
1
Simula Research Laboratory, Norway
;
2
Swedish Institute of Computer Science, Sweden
Keyword(s):
Test Suite Reduction, Software Product Line Testing.
Related
Ontology
Subjects/Areas/Topics:
Software Engineering
;
Software Engineering Methods and Techniques
;
Software Testing and Maintenance
Abstract:
In many cases, Software Product Line Testing (SPLT) targets only the selection of test cases which cover product features or feature interactions. However, higher testing efficiency can be achieved through the selection of test cases with improved fault-revealing capabilities. By associating each test case a priority-value representing (or aggregating) distinct criteria, such as importance (in terms of fault discovered in previous test campaigns), duration or cost, it becomes possible to select a feature-covering test suite with improved capabilities. A crucial objective in SPLT then becomes to identify a test suite that optimizes reaching a specific goal (lower test duration or cost), while preserving full feature coverage. In this paper, we revisit this problem with a new approach based on constraint optimization with a special constraint called GLOBAL CARDINALITY and a sophisticated search heuristic. This constraint enforces the coverage of all features through the computation of
max flows in a network flow representing the coverage relation. The computed max flows represent possible solutions which are further processed in order to determine the solution that optimizes the given objective function, e.g., the lowest test execution costs. Our approach was implemented in a tool called Flower/C and experimentally evaluated on both randomly generated instances and industrial case instances. Comparing Flower/C with MINTS (Minimizer for Test Suites), the State-Of-the-Art tool based on an integer linear formulation for performing similar test suite optimization, we show that our approach either outperforms MINTS or has comparable performance on random instances. On industrial instances, we compared three distinct models of Flower/C (using distinct global constraints) and the one mixing distinct constraints showed excellent performances with high reduction rates. These results opens door to an industrial adoption of the proposed technology.
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