Design and analysis of different alternating variable searches for search-based software testing

Manual software testing is a notoriously expensive part of the software development process, and its automation is of high concern. One aspect of the testing process is the automatic generation of test inputs. This paper studies the Alternating Variable Method (AVM) approach to search-based test input generation. The AVM has been shown to be an effective and efficient means of generating branch-covering inputs for procedural programs. However, there has been little work that has sought to analyse the technique and further improve its performance. This paper proposes two different local searches that may be used in conjunction with the AVM, Geometric and Lattice Search. A theoretical runtime analysis proves that under certain conditions, the use of these searches results in better performance compared to the original AVM. These theoretical results are confirmed by an empirical study with five programs, which shows that increases of speed of over 50% are possible in practice

Precision, Local Search and Unimodal Functions

We investigate the effects of precision on the efficiency of various local search algorithms on 1-D unimodal functions. We present a (1 + 1)-EA with adaptive step size which finds the optimum in O(log n) steps, where n is the number of points used. We then consider binary (base-2) and reflected Gray code representations with single bit mutations. The standard binary method does not guarantee locating the optimum, whereas using the reflected Gray code does so in �((log n) 2) steps. A(1 + 1)-EA with a fixed mutation probability distribution is then presented which also runs in O((log n) 2). Moreover, a recent result shows that this is optimal (up to some constant scaling factor), in that there exist unimodal functions for which a lower bound of �((log n) 2) holds regardless of the choice of mutation distribution. For continuous multimodal functions, the algorithm also locates the global optimum in O((log n) 2). Finally, we show that it is not possible for a black box algorithm to efficiently optimise unimodal functions for two or more dimensions (in terms of the precision used)

Precision, Local Search and Unimodal Functions

We investigate the effects of precision on the efficiency of various local search algorithms on 1-D unimodal functions. We present a (1 + 1)-EA with adaptive step size which finds the optimum in O(log n) steps, where n is the number of points used. We then consider binary and Gray representations with single bit mutations. The standard binary method does not guarantee locating the optimum, whereas using Gray code does so in O((log n) 2) steps. A (1 + 1)-EA with a fixed mutation probability distribution is then presented which also runs in O((log n) 2). Moreover, a recent result shows that this is optimal (up to some constant scaling factor), in that there exist unimodal functions for which a lower bound of Ω((log n) 2) holds regardless of the choice of mutation distribution. Finally, we show that it is not possible for a black box algorithms to efficiently optimise unimodal functions for two or more dimensions (in terms of the precision used)