Reliability Measurement of Digital Forensic Open Source Tools Using Fuzzy Logic

In this paper, a fuzzy logic based optimization technique has been adopted to accurately measure software reliability. In the literature, methods like, multiple linear regression, multivariate adaptive regression splines, back propagation trained neural network, dynamic evolving neuro-fuzzy inference system and TreeNet are available. Even a number of models are predicted for software reliability by ensembling the one or more of the above mentioned methods. It has been seen that software reliability cannot be uniformly treated by any one of the above mentioned method as the parameters of software reliability differs in weights depending upon the type of the application. Any application would need a defined recipe of ingredients like- interoperability, scalability, evolvability, pluggability, dynamicity, accuracy, security, cost optimality etc. One or more elements may prevail upon other and be considered as control variables in the optimization techniques applied. The concept calls for an optimization technique when weight can be changed dynamically in parameters depending on the conditionality of responses of the system or the user. This paper introduces the concept of shifting reliability based on dynamic decision making over the various control variables by changing their weight within time-in period in a real time system. Measurement of software reliability, in particular shifting reliability will make the software reliability prediction much more pragmatic in real time system. Digital forensic is in need for a suitable shifting reliability measurement technique. software reliability in case of DF tools governs the legal use. A number of open source tools are waiting for their professional run for want of reliability testing. This paper suggests software reliability testing, even for testing for shifting reliability. A method which has been adapted from simplex method for fuzzy variable linear programming problem has been applied to the particular case study. Typical formulation of fuzzy optimization depends on piece wise linear membership function. Linear member ship function introduces regions of no differentiability. In fact no deterministic method can be applied for fuzzy optimization, utilization of continuous differentiable membership function permits the use of gradient base methods. An expression for the gradient base decision degree function is available in literature. This formulation has been applied in a case study having a number of control variables using a suitable tool e.g. ProjectSixPap.