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

Genetic algorithms

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Branch-coverage testability transformation for unstructured programs

Test data generation by hand is a tedious, expensive and error-prone activity, yet testing is a vital part of the development process. Several techniques have been proposed to automate the generation of test data, but all of these are hindered by the presence of unstructured control flow. This paper addresses the problem using testability transformation. Testability transformation does not preserve the traditional meaning of the program, rather it deals with preserving test-adequate sets of input data. This requires new equivalence relations which, in turn, entail novel proof obligations. The paper illustrates this using the branch coverage adequacy criterion and develops a branch adequacy equivalence relation and a testability transformation for restructuring. It then presents a proof that the transformation preserves branch adequacy

An interacting replica approach applied to the traveling salesman problem

We present a physics inspired heuristic method for solving combinatorial optimization problems. Our approach is specifically motivated by the desire to avoid trapping in metastable local minima- a common occurrence in hard problems with multiple extrema. Our method involves (i) coupling otherwise independent simulations of a system ("replicas") via geometrical distances as well as (ii) probabilistic inference applied to the solutions found by individual replicas. The {\it ensemble} of replicas evolves as to maximize the inter-replica correlation while simultaneously minimize the local intra-replica cost function (e.g., the total path length in the Traveling Salesman Problem within each replica). We demonstrate how our method improves the performance of rudimentary local optimization schemes long applied to the NP hard Traveling Salesman Problem. In particular, we apply our method to the well-known "$k$-opt" algorithm and examine two particular cases- $k=2$ and $k=3$. With the aid of geometrical coupling alone, we are able to determine for the optimum tour length on systems up to $280$ cities (an order of magnitude larger than the largest systems typically solved by the bare $k=3$ opt). The probabilistic replica-based inference approach improves $k-opt$ even further and determines the optimal solution of a problem with $318$ cities and find tours whose total length is close to that of the optimal solutions for other systems with a larger number of cities.Comment: To appear in SAI 2016 conference proceedings 12 pages,17 figure

Pattern-Based Genetic Algorithm for Airborne Conflict Resolution

NASA has developed the Autonomous Operations Planner (AOP) airborne decision support tool to explore advanced air traffic control concepts that include delegating separation authority to aircraft. A key element of the AOP is its strategic conflict resolution (CR) algorithm, which must resolve conflicts while maintaining conformance with traffic flow management constraints. While a previous CR algorithm, which focused on broader flight plan optimization objectives as a part of conflict resolution, had successfully been developed, new research has identified the need for resolution routes the users find more acceptable (i.e., simpler and more intuitive). A new CR algorithm is presented that uses a combination of pattern-based maneuvers and a genetic algorithm to achieve these new objectives. Several lateral and vertical maneuver patterns are defined and the application of the genetic algorithm explained. A new approach to defining a conflicted fitness function using estimates of the local conflict region around a conflicted trajectory is also presented. Preliminary performance characteristics of the implemented algorithm are provided