National Institute of Neurological Disorders and Stroke

An Adaptive Polyploid Memetic Algorithm for scheduling trucks at a cross-docking terminal

This paper presents a tabu search metaheuristic algorithm for the automatic generation of structural software tests. It is a novel work since tabu search is applied to the automation of the test generation task, whereas previous works have used other techniques such as genetic algorithms. The developed test generator has a cost function for intensifying the search and another for diversifying the search that is used when the intensification is not successful. It also combines the use of memory with a backtracking process to avoid getting stuck in local minima. Evaluation of the generator was performed using complex programs under test and large ranges for input variables. Results show that the developed generator is both effective and efficient.

A tabu search algorithm for structural software testing.

R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data.

Software testing is the most critical phase of the Software Development Life Cycle. Software under test goes through various phases, which as per the study are test analysis; test planning, test case/data/environment preparation, test execution, bug logging and tracking and closure. There is lot of research which has been done in past to optimize overall testing process with intent of improving quality of software in a minimum amount of time. After evaluating all available testing processes it has been found that different development models are used for different types of applications and different testing techniques are performed to test the same. Based on the research during the study of this paper, it has been analyzed that each company modifies their testing process as per the needs and performs testing based on the criticality of the applications. The most critical components of each application have to be tested thoroughly to ensure their functional, performance and security features are behaving as expected. This paper talks about ensuring the quality of all types of software applications by performing certain types of testing techniques and optimized software testing processes. As per the study and research done testing types can be categorized under three major testing techniques which are Functional, Performance and Security Testing and major software testing process called as Analysis, Preparation and Execution and closure.

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Software Test Process, Testing Types and Techniques

Black Box Testing is used when code of the module is not available. In such situations appropriate priorities can be given to different test cases, so that the quality of software is not compromised, if testing is to be stopped prematurely. This paper proposes a framework, which uses requirement analysis and design specification, to prioritize the test cases. The work would be beneficial to both practitioners and researchers.

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Black Box Testing based on Requirement Analysis and Design Specifications

Diploidity is the essence of the nature. However, it has largely been ignored by the computer science fraternity. Simple Genetic Algorithms and their variants have extensively been used in solving NP hard problems in-spite of the fact that Diploid Genetic Algorithms assure robustness as against Simple Genetic Algorithms which solitary guarantee optimization. Moreover, the past endeavors proved that these algorithms are more successful in dynamic environments as compared to their haploid counterpart. The work proves the above point by applying Diploid genetic Algorithms to Dynamic Travelling Salesman Problem and comparing the results to Greedy Approach and Simple Genetic Algorithms. The work also presents a hybrid approach namely Greedy Genetic Approach. The results of the experiments proved the fact that diploidity ensures robustness. In the experiments carried out, the three variants of dominance were implemented and 115 trials bought forth the point that though Haploid and Greedy Approaches do not outperform the other, Diploid are the best bet for dynamic environments.

On the Applicability of Diploid Genetic Algorithms in Dynamic Environments

Puzzle problem is an important problem in mathematics and has implications in Artificial Intelligence especially in gaming. The work presented reviews the previous attempts to solve this problem. A formal definition of the problem has been presented. The reason why it is considered as NP hard problem and why Genetic Algorithms (GAs) is applied has been explained. The work here by presents a GAs based algorithm to solve N - Puzzle problem. The algorithm has been analyzed and it is a sturdy belief that the presented algorithm has complexity better than most of the works studied. The work is a part of larger endeavor to solve all NP Hard problems by GAs.

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Genetic based Algorithm for N - Puzzle Problem

The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness is more important. However, there is a need to review the work that has been done so far in the field. It is also important to analyse the applicability of the premise of the dominance techniques applied so far in order to implement the technique. The work presents a systematic review of diploid genetic algorithms, examines the premise of the dominance relation used in different works and discusses the future scope. The work also discusses the biological basis of evaluating dominance. The work is important as the future of machine learning relies on techniques that are robust as well as efficient.

On the applicability of diploid genetic algorithms

Test Data Generation is the soul of automated testing. The dream of having efficient and robust automated testing software can be fulfilled only if the task of designing a robust automated test data generator can be accomplished. In the work we explore the gaps in the existing techniques and intend to fill these gaps by proposing new algorithms. The following work presents algorithms that handle almost all the constructs of procedural programming languages. The proposed technique uses cellular automata as its base. The use of Cellular Automata brings a blend of artificial life to the work. The work is a continuation of our earlier attempt to amalgamate Cellular Automata based algorithms to generate test data. The technique has been applied to C programs and is currently being tested on a financial enterprise resource planning system. Since, the solution of most of the problems can be found by observing nature, we must explore artificial nature to accomplish the above task.

Harsh Bhasin

Papers

Black Box Testing based on Requirement Analysis and Design Specifications

On the Applicability of Diploid Genetic Algorithms in Dynamic Environments

Genetic based Algorithm for N - Puzzle Problem

On the applicability of diploid genetic algorithms

Cellular-genetic test data generation