184,772 research outputs found
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
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An analysis of test data selection criteria using the RELAY model of fault detection
RELAY is a model of faults and failures that defines failure conditions, which describe test data for which execution will guarantee that a fault originates erroneous behavior that also transfers through computations and information flow until a failure is revealed. This model of fault detection provides a framework within which other testing criteria's capabilities can be evaluated. In this paper, we analyze three test data selection criteria that attempt to detect faults in six fault classes. This analysis shows that none of these criteria is capable of guaranteeing detection for these fault classes and points out two major weaknesses of these criteria. The first weakness is that the criteria do not consider the potential unsatisfiability of their rules; each criterion includes rules that are sufficient to cause potential failures for some fault classes, yet when such rules are unsatisfiable, many faults may remain undetected. Their second weakness is failure to integrate their proposed rules; although a criterion may cause a subexpression to take on an erroneous value, there is no effort made to guarantee that the intermediate values cause observable, erroneous behavior. This paper shows how the RELAY model overcomes these weaknesses
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
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