Heuristic Approach of Automated Test Data Generation for Program having Array of Different Dimensions and Loops with Variable Number of Iteration

TL;DR

This paper introduces a heuristic method for automated test data generation targeting programs with loops and arrays of varying dimensions, aiming to improve coverage efficiently.

Contribution

It proposes a novel heuristic approach using path-level parameters to predict minimal iterations for test data generation in complex loop and array scenarios.

Findings

Effective in handling nested loops and variable array sizes

Reduces path explosion by using heuristics and filtering

Demonstrated on algorithms like merge sort and matrix multiplication

Abstract

Normally, program execution spends most of the time on loops. Automated test data generation devotes special attention to loops for better coverage. Automated test data generation for programs having loops with variable number of iteration and variable length array is a challenging problem. It is so because the number of paths may increase exponentially with the increase of array size for some programming constructs, like merge sort. We propose a method that finds heuristic for different types of programming constructs with loops and arrays. Linear search, Bubble sort, merge sort, and matrix multiplication programs are included in an attempt to highlight the difference in execution between single loop, variable length array and nested loops with one and two dimensional arrays. We have used two parameters/heuristics to predict the minimum number of iterations required for generating automated test data. They are longest path level (kL) and saturation level (kS). The proceedings of our work includes the instrumentation of source code at the elementary level, followed by the application of the random inputs until all feasible paths or all paths having longest paths are collected. However, duplicate paths are avoided by using a filter. Our test data is the random numbers that cover each feasible path.