ASSESSING SOFTWARE RELIABILITY USING GENETIC ALGORITHMS

Rachna Jain, Arun Sharma

Abstract


The role of software reliability and quality improvement is becoming more important than any other issues related to software development. To date, we have various techniques that give a prediction of software reliability like neural networks, fuzzy logic, and other evolutionary algorithms.  A genetic algorithm has been explored for predicting software reliability.  One of the important aspects of software quality is called software reliability, thus, software engineering is of a great place in the software industry. To increase the software reliability, it is mandatory that we must design a model that predicts the fault and error in the software program at early stages, rectify them and then increase the functionality of the program within a minimum time and in a low cost. There exist numerous algorithms that predict software errors such as the Genetic Algorithm, which has a very high ability to predict software bugs, failure and errors rather than any other algorithm. The main purpose of this paper is to predict software errors with so precise, less time-consuming and cost-effective methodology. The outcome of this research paper is showing that the rates of applied methods and strategies are more than 96 percent in ideal conditions. 


Keywords


Evolutionary algorithms; Genetic algorithm; Faulty /non-faulty data analysis; Software reliability engineering.

Full Text:

PDF

References


Berndt D, Fisher J, Johnson L, Pinglikar J, Watkins A (2003), Breeding software test cases with genetic algorithms. In System Sciences, Proceedings of the 36th Annual Hawaii International Conference on. IEEE 338–347.

Bishnu P.S, Bhattacherjee V (2011), Application of K-medoids with kd-tree for software fault prediction. IEEE Transactions Software Engineer-ing 321-452.

Briand L, Daly W, Wüst J, Porter D (2000), Exploring the relationships between design measures and software quality. Journal of Systems and Software 245-273.

CaoY, Hu C, Li L (2009), An approach to generate software test data for a specific path automatically with genetic algorithm. International Conference on Reliability, Maintainability and Safety 888-892.

Catal C, Sevim U, Diri B (2009), Clustering and metrics thresholds based software fault prediction of unlabelled program modules. Sixth International Conference on Information Technology: New Generations 199-204.

De J, Spears W.M (1989). Using genetic algorithms to solve NP-complete problems. In ICGA 124-132.

Dua A, Mishra G (2002), Stochastic search technique for solving constrained optimization problems with multiple objectives. Proc of National Conference on Emerging Convergent Technologies and Systems 399-404.

Goldberg D.E (1987), Genetic algorithms in search, optimisation, and machine learning. Proc. of the 2nd Int. Conf. on Genetic Algorithms, Addison Wesley 41 –49.

Goldberg D (1989), Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, Massachusetts 60-309.

Goldberg D.E, Deb K, Clark J.H (1992), Genetic algorithms, noise, and the sizing of populations. Complex Systems 333–362.

Ghiduk, Ahmed S, Girgis M.R (2010), Using genetic algorithms and dominance concepts for generating reduced test data, Informatica Slovenia 377-385.

Gupta N.K, Rohil M.K (2008), Using genetic algorithm for unit testing of object oriented software. International Conference on Emerging Trends in Engineering and Technology 308-313.

Houck Christopher R, Joines A, Kay J.G.M (1995), A genetic algorithm for function optimization. A Matlab Implementation 1-10.

Kim S, Zhang H, Wu R, Gong L (2011), Dealing with noise in defect prediction. CSE‟11, Waikiki, Honolulu, HI, USA 481-490.

Kumar R, Gupta N (2015), Reliability measurement of object oriented design: Complexity Perspective. International Advanced Research Journal in Science, Engineering and Technology 38-44.

Mishra, Dubey S.K (2016), Reliability of object oriented software using fuzzy approach. ISO/IEC 9126 model and CK Metrics 398-401.

Nirpal, Premal B, Kale K.V (2010), Comparison of software test data for automatic path coverage using genetic algorithm. Internal Journal of Computer Science and Engineering Technology 42-48.

Oliveira E, Pozo A, Vergilio S.R (2006), Using boosting techniques to improve software reliability models based on genetic programming. 18th IEEE International Conference on Tools with Artificial Intelligence 643-650.

Oliveira E, Silia C (2005), Modelling software reliability growth with genetic programming. Proceedings of the 16th IEE International Symposium on Software Reliability Engineering 39-121.

Pham H, Nordmann (1999), A general imperfect -software-debugging model with s-shaped fault-detection rate. IEEE Trans. Reliability 169–175.

Quinlan J.R (1993), Programs for machine learning. Morgan Kaufmann Publishers 235-240.

Rajappa V, Biradar A, Panda S (2008), Effective software test case generation using genetic algorithm based graph theory. First International Conference on Emerging Trends in Engineering and Technology 298-303.

Rauf A, Anwar S, Jaffer M.A, Shahid A.A (2010), Automated GUI test coverage analysis using GA. 7th International Conference on Information Technology New Generations 1057-1062.

Sharma C, Dubey S.K. (2015), A perspective approach of software reliability models and techniques. ARPN Journal of Engineering and Applied Sciences 7300-7308

Srivastava P.R. Kim T.H (2009), Application of genetic algorithm in software testing. International Journal of Software Engineering and its Applications 87-96.

Sheta A (2006), Reliability growth modelling for software fault detection using particle swarm optimization. IEEE Congress on Evolutionary Computation 10428–10435.

Yamada S, Ohba (1983), S-shaped reliability growth modelling for software error detection. IEEE Trans. Reliability 475–484.




DOI: http://dx.doi.org/10.24200/tjer.vol16iss1pp11-17

Refbacks

  • There are currently no refbacks.




Copyright (c) 2019 Rachna Jain, Arun Sharma

Creative Commons License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

TJER 2017-CC BY-ND

This journal and its content is licensed under a Attribution-NoDerivatives 4.0 International.

Flag Counter