A survey on Test Suite Reduction frameworks and tools <s> 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> 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 </s> A survey on Test Suite Reduction frameworks and tools <s> Integer Linear Programming based TSR Tools <s> In situ testing techniques have become an important means of ensuring the reliability of embedded systems after they are deployed in the field. However, these techniques do not help testers optimize the energy consumption of their in situ test suites, which can needlessly waste the limited battery power of these systems. In this work, we extend prior techniques for test suite minimization in such a way as to allow testers to generate energy-efficient, minimized test suites with only minimal modifications to their existing work flow. We perform an extensive empirical evaluation of our approach using the test suites provided for real world applications. The results of the evaluation show that our technique is effective at generating, in less than one second, test suites that consume up to 95% less energy while maintaining coverage of the testing requirements. <s> BIB003
To the best of our knowledge, researchers have proposed two tools namely, MINTS BIB001 ) and EDTSO BIB002 , which are based on encoding TSR problem as an ILP problem.  MINTS: Hsu and Orso BIB001 ) proposed a framework to support a wide range of TSR problems by handling various objectives using ILP-based approach. The proposed framework is supported by a tool, called MINTS that provides the flexibility of specifying multi-objective TSR problems. Based on the defined encoding mechanism, related objectives are given directly to one or more supporting ILP solvers. The proposed tool is freely available and due to its modular structure it can be easily plug-in different ILP solvers. The authors experimentally evaluated the performance of the tool and concluded that MINTS is effective and practical to testsuite size problem.  EDTSO: Li et al. BIB002 proposed a novel approach in order to optimize the energy usage of test suites. The proposed approach is supported by a prototype tool, called EDTSO, which focuses on an energy-efficient reduced test suite. EDTSO is effective for testing Android applications having limited energy budget by automatically producing energy-efficient RS. The authors performed a pair-wise comparison of the energy usage of the RS, including variability, potential impact, and effectiveness as determined by the proposed approach and traditional approach. They reported that EDTSO maintained coverage along with minimal energy consumption ranging from 5% to 48% compared to traditional TSR techniques. Later, Li et al. BIB003 extended EDTSO by integrating it with existing test workflows and also by utilizing other resource constraints such as test execution time.