A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Amorphous slicing of extended finite state machines

Slicing is useful for many Software Engineering applications and has been widely studied for three decades, but there has been comparatively little work on slicing Extended Finite State Machines (EFSMs). This paper introduces a set of dependency based EFSM slicing algorithms and an accompanying tool. We demonstrate that our algorithms are suitable for dependence based slicing. We use our tool to conduct experiments on ten EFSMs, including benchmarks and industrial EFSMs. Ours is the first empirical study of dependence based program slicing for EFSMs. Compared to the only previously published dependence based algorithm, our average slice is smaller 40% of the time and larger only 10% of the time, with an average slice size of 35% for termination insensitive slicing

Predicting Whole Forest Structure, Primary Productivity, and Biomass Density From Maximum Tree Size and Resource Limitations

In the face of uncertain biological response to climate change and the many critiques concerning model complexity it is increasingly important to develop predictive mechanistic frameworks that capture the dominant features of ecological communities and their dependencies on environmental factors. This is particularly important for critical global processes such as biomass changes, carbon export, and biogenic climate feedback. Past efforts have successfully understood a broad spectrum of plant and community traits across a range of biological diversity and body size, including tree size distributions and maximum tree height, from mechanical, hydrodynamic, and resource constraints. Recently it was shown that global scaling relationships for net primary productivity are correlated with local meteorology and the overall biomass density within a forest. Along with previous efforts, this highlights the connection between widely observed allometric relationships and predictive ecology. An emerging goal of ecological theory is to gain maximum predictive power with the least number of parameters. Here we show that the explicit dependence of such critical quantities can be systematically predicted knowing just the size of the largest tree. This is supported by data showing that forests converge to our predictions as they mature. Since maximum tree size can be calculated from local meteorology this provides a general framework for predicting the generic structure of forests from local environmental parameters thereby addressing a range of critical Earth-system questions.Comment: 26 pages, 4 figures, 1 Tabl