Abstract
Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples; the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully; the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.
Highlights
In search-based software engineering, researchers have been interested in the automated test data generation so that it would be helpful for testing the software
From the fore-mentioned studies, in this paper, we introduce various strategies to construct genetic algorithms (GAs) [4]1 in generating the worstcase instance which is defined in the aspect of algorithm performance
We say that two GAs are the same if and only if they are for the same problem and they belong to the same classification; we propose the classification in Tables 3-5, respectively for each problem
Summary
In search-based software engineering, researchers have been interested in the automated test data generation so that it would be helpful for testing the software. In the part of non-functional testing, these studies had a bias to generate the test data that show the best/worst-case execution time. If we analyze an algorithm, not the entire program, there can be many measures other than the execution time, in terms of non-functional testing. The trials did not introduce various strategies for constructing metaheuristics to generate the worst-case instance. From the fore-mentioned studies, in this paper, we introduce various strategies to construct genetic algorithms (GAs) [4]1 in generating the worstcase (problem) instance which is defined in the aspect of algorithm performance. We try to find the worst-case instance of three example problems for some algorithms; the (internal) sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP) These are well-known problems and each can show different strategy to construct a GA.
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