Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction(MOMPSO)

Software testing plays a pivotal role in entire software development lifecycle.It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required.These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite,fault-tolerance,defects due to uncovered-statements and overall-performance at the time of execution.With this objective,the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization.The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate(90.78%to 100%)and failure detection rate(44.56%to 55.01%).Results proved MOMPSO outperformed the other stated algorithms.

SPIDER:Speeding up Side-Channel Vulnerability Detection via Test Suite Reduction

Side-channel attacks allow adversaries to infer sensitive information,such as cryptographic keys or private user data,by monitoring unintentional information leaks of running programs.Prior side-channel detection methods can identify numerous potential vulnerabilities in cryptographic implementations with a small amount of execution traces due to the high diffusion of secret inputs in crypto primitives.However,because non-cryptographic programs cover different paths under various sensitive inputs,extending existing tools for identifying information leaks to non-cryptographic applications suffers from either insufficient path coverage or redundant testing.To address these limitations,we propose a new dynamic analysis framework named SPIDER that uses fuzzing,execution profiling,and clustering for a high path coverage and test suite reduction,and then speeds up the dynamic analysis of side-channel vulnerability detection in non-cryptographic programs.We analyze eight non-cryptographic programs and ten cryptographic algorithms under SPIDER in a fully automated way,and our results confirm the effectiveness of test suite reduction and the vulnerability detection accuracy of the whole framework.

A Usable Selection Range Standard Based on Test Suite Reduction Algorithms

In this paper, we analyze the features and distinctions of 6 classical algorithms： greedy algorithm （G）, greedy evolution algorithm （GE）, heuristics algorithm （H）, greedy heuristic G （GRE）, integer linear programming algorithm （ILP） and genetic algorithm （GA） to ensure the main influencing factors-the performance of algorithms and the running time of algorithms. What＇s more, we would not only present a research design that aims at gaining deeper understanding about the algorithm classification and its function as well as their distinction, but also make an empirical study in order to obtain a practical range standard that can guide the selection of reduction algorithms. When the size of a test object （product of test requirements and test cases） is smaller than 2000×2000, G algorithm is the commonly recommended algorithm. With the growth of test size, the usage of GE and GRE becomes more general.