A survey on Test Suite Reduction frameworks and tools <s> Multiple Server-based TSR Optimization <s> Automated cost-effective test strategies are needed to provide reliable, secure, and usable Web applications. As a software maintainer updates an application, test cases must accurately reflect usage to expose faults that users are most likely to encounter. User-session-based testing is an automated approach to enhancing an initial test suite with real user data, enabling additional testing during maintenance as well as adding test data that represents usage as operational profiles evolve. Test suite reduction techniques are critical to the cost effectiveness of user-session-based testing because a key issue is the cost of collecting, analyzing, and replaying the large number of test cases generated from user-session data. We performed an empirical study comparing the test suite size, program coverage, fault detection capability, and costs of three requirements-based reduction techniques and three variations of concept analysis reduction applied to two Web applications. The statistical analysis of our results indicates that concept analysis-based reduction is a cost-effective alternative to requirements-based approaches. <s> BIB001 </s> A survey on Test Suite Reduction frameworks and tools <s> Multiple Server-based TSR Optimization <s> The latest developments in mobile computing technology have changed user preferences for computing. However, in spite of all the advancements in the recent years, Smart Mobile Devices (SMDs) are still low potential computing devices which are limited in memory capacity, CPU speed and battery power lifetime. Therefore, Mobile Cloud Computing (MCC) employs computational offloading for enabling computationally intensive mobile applications on SMDs. However, state-of-the-art computational offloading frameworks lack of considering the additional overhead of components migration at runtime. Therefore resources intensive and energy consuming distributed application execution platform is established. This paper proposes a novel distributed Energy Efficient Computational Offloading Framework (EECOF) for the processing of intensive mobile applications in MCC. The framework focuses on leveraging application processing services of cloud datacenters with minimal instances of computationally intensive component migration at runtime. As a result, the size of data transmission and energy consumption cost is reduced in computational offloading for MCC. We evaluate the proposed framework by benchmarking prototype application in the real MCC environment. Analysis of the results show that by employing EECOF the size of data transmission over the wireless network medium is reduced by 84 % and energy consumption cost is reduced by 69.9 % in offloading different components of the prototype application. Hence, EECOF provides an energy efficient application layer solution for computational offloading in MCC. <s> BIB002
The use of multiple servers is beneficial in improving the computational time, especially for large complex systems having an enormous pool of test cases BIB001 . So, in this situation, the tester can achieve efficient computational time at the cost of extra required servers. This could be feasible in a situation, where divide-and-conquer strategy is acceptable. For instance, first the optimization problem is divided into many sub-problems, and then each sub-problem is independently solved using a single server. Thus, the proposed tool can act as a controller to manage multiple servers. It first determines the sub-problems and assigns each sub-problem to the available server for optimization. Next, the tool collects the computed solutions from each server and merges into a single solution. After that, the tool removes the duplication from the obtained solutions (from multiple servers) to generate a final optimal solution. However, resource affordability plays a vital role in the adoption of concurrent processing. This could be possible if low-cost servers are available or the tester has sufficient computational resources. One feasible solution is to employ Cloud Computing BIB002 for achieving low-cost computational resources without compromising the quality of the obtained optimized solution.