Many programs can be configured through dynamic and/or static selection of configuration variables. A software product line (SPL), for example, specifies a family of programs where each program is defined by a unique combination of features. Systematically testing SPL programs is expensive as it can require running each test against a combinatorial number of configurations. Fortunately, a test is often independent of many configuration variables and need not be run against every combination. Configurations that are not required for a test can be pruned from execution. This paper presents SPLat, a new way to dynamically prune irrelevant configurations: the configurations to run for a test can be determined during test execution by monitoring accesses to configuration variables. SPLat achieves an optimal reduction in the number of configurations and is lightweight compared to prior work that used static analysis and heavyweight dynamic execution. Experimental results on 10 SPLs written in Java show that SPLat substantially reduces the total test execution time in many cases. Moreover, we demonstrate the scalability of SPLat by applying it to a large industrial code base written in Ruby on Rails.
Configurable systems are those that can be adapted from a set of options. They are prevalent and testing them is important and challenging. Existing approaches for testing configurable systems are either unsound (i.e., they can miss fault-revealing configurations) or do not scale. This paper proposes EvoSPLat, a regression testing technique for configurable systems. EvoSPLat builds on our previouslydeveloped technique, SPLat, which explores all dynamically reachable configurations from a test. EvoSPLat is tuned for two scenarios of use in regression testing: Regression Configuration Selection (RCS) and Regression Test Selection (RTS). EvoSPLat for RCS prunes configurations (not tests) that are not impacted by changes whereas EvoSPLat for RTS prunes tests (not configurations) which are not impacted by changes. Handling both scenarios in the context of evolution is important.Experimental results show that EvoSPLat is promising. We observed a substantial reduction in time (∼22%) and in the number of configurations (∼45%) for configurable Java programs. In a case study on a large real-world configurable system (GCC), EvoSPLat reduced ∼35% of the running time. Comparing EvoSPLat with sampling techniques, 2-wise was the most efficient technique, but it missed two bugs whereas EvoSPLat detected all bugs four times faster than 6-wise, on average.
A software product line (SPL) is a family of programs that are differentiated by features -increments in functionality. Systematically testing an SPL is challenging because it requires running each test of a test suite against a combinatorial number of programs. Feature models capture dependencies among features and can (1) reduce the space of programs to test and (2) enable accurate categorization of failing tests as failures of programs or the tests themselves, not as failures due to illegal combinations of features. In practice, sadly, feature models are not always available.We introduce SPLif, the first approach for testing SPLs that does not require the a priori availability of feature models. Our insight is to use a profile of passing and failing test runs to quickly identify failures that are indicative of real problems in test or code rather than specious failures due to illegal feature combinations.Experimental results on five SPLs and one large configurable system (GCC) demonstrate the effectiveness of our approach. SPLif enabled the discovery of five news bugs in GCC, three of which have already been fixed.
CCS Concepts•Software and its engineering → Software verification and validation;
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