A survey on Test Suite Reduction frameworks and tools <s> Comparison of Integer Linear Programming based TSR Tools <s> Test-suite minimization techniques aim to eliminate redundant test cases from a test-suite based on some criteria, such as coverage or fault-detection capability. Most existing test-suite minimization techniques have two main limitations: they perform minimization based on a single criterion and produce suboptimal solutions. In this paper, we propose a test-suite minimization framework that overcomes these limitations by allowing testers to (1) easily encode a wide spectrum of test-suite minimization problems, (2) handle problems that involve any number of criteria, and (3) compute optimal solutions by leveraging modern integer linear programming solvers. We implemented our framework in a tool, called MINTS, that is freely-available and can be interfaced with a number of different state-of-the-art solvers. Our empirical evaluation shows that MINTS can be used to instantiate a number of different test-suite minimization problems and efficiently find an optimal solution for such problems using different solvers. <s> BIB001 </s> A survey on Test Suite Reduction frameworks and tools <s> Comparison of Integer Linear Programming based TSR Tools <s> Post-deployment in situ testing and validation techniques have become an important means of ensuring the reliability of mobile and embedded systems. However, these techniques do not take into consideration the amount of energy they consume, which is an issue of paramount concern for systems with limited energy budgets. In this paper we propose a new test suite minimization approach that allows developers to generate energy-efficient, minimized test suites. The approach is based on encoding minimization problems as integer linear programming problems. Our empirical evaluation shows that, compared to traditionally generated minimized test suites, the test suites generated by our approach are equally effective in terms of their test coverage, but can realize energy savings of up to 90 %. <s> BIB002
Integer Linear Programming (ILP) based TSR tools, including MINTS BIB001 and EDTSO BIB002 , determined an optimal solution using multiple ILP solvers by considering defined objectives and constraints. In addition, improved execution time is achieved using a wide range of ILP solvers by running multiple solvers in parallel fashion and then simplifying the generated solutions in a sequential manner. MINTS provides any set of testrelated data according to the need of a testing environment. Appropriate selection of test data greatly affects the outcome of MINTS, which heavily depends on the tester's expertise. Furthermore, non-availability of historical testing information makes MINTS impractical to compute the optimal solution. Moreover, both MINTS and EDTSO need to execute each test case in order to collect profiles such as time or memory complexity and test-code structural coverage by instrumenting the targeted program. Certainly, high execution cost is required for instrumentation, which might be a key concern while employing these TSR tools.