AP-PSO algorithm-based combined test case generation method

Abstract

The invention discloses an AP-PSO algorithm-based combined test case generation method and belongs to the field of software testing. The method comprises the steps of obtaining a combination set needed to be covered through static analysis; adjusting a one-test-at-a-time policy, changing random selection of one combination to construction of one priority measurement function, performing priority measurement on each combination, and selecting a combination with the highest priority; and generating a single test case by adopting an adaptive particle swarm optimization algorithm, performing adaptive adjustment on inertial weight based on swarm particle quality, performing control on a maximum speed of particles, performing rounding operation on the speed of each particle in a discretization way, and performing boundary processing on particle positions by using a reflective wall policy, thereby preventing an effective search space from flying out. The method can be suitable for generation of coverage tables with different coverage degrees; the scale of combined test case sets is effectively reduced; and the combined test case generation efficiency is greatly improved.

Description

technical field [0001] The invention belongs to the field of software testing, and in particular relates to a method for generating combined test cases based on an AP-PSO algorithm. Background technique [0002] Software testing is a crucial link in software engineering to ensure software quality. An ideal software testing method needs to have the characteristics of high error detection capability, low cost consumption and wide applicability. As a protocol-based software testing method, combinatorial testing aims to select a small number of effective test cases from the huge combinatorial space faced by the software to be tested, and generate a set of test cases with high coverage and strong error-discovering ability. [0003] As a relatively new heuristic search algorithm, the particle swarm optimization algorithm has the characteristics of easy understanding, easy implementation, and strong global search ability, and is currently also used in this field. At present, in th...

AI Technical Summary

Problems solved by technology

At present, in the related research of using particle swarm optimization to generate coverage tables, it mainly realizes the generation of two-two combination coverage test cases, and does not consider the situation of higher coverage strength and inconsistent interaction strength

At the same time, the selection of parameters has a great impact on the performance of the algorithm. Most of the research work usually selects a set of empirical parameters without considering the particularity of parameter selection on different coverage tables.

Images