Hybrid intelligent model for software maintenance prediction

Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used for evolving the neural network topology until an optimal topology is reached. The model was applied on a popular open source program, namely, Android. The results are very promising, where the correlation between actual and predicted points reaches 0.9

A Review on Software Architecture Optimization Methods

Due to the remarkable mechanical request for programming frameworks, the expansion of the uncertainty, the quality requirements and quality of testing, the programming engineering configuration has been transformed into essential progression movement and the examination site is developing rapidly. In the recent decades, programming engineering involves improved technologies, which means to organize a scan for design outline for an arrangement of value attributes, have multiplied. In any case, the results shown are divided into different research groups, many framework areas and different quality features. Coming about the inclusion of current research, we have played a well-structured writing survey and have broken the result of various check-sheets of different research groups. Considering this study, a scientific classification has been done which is used for current research. Apart from this, the effective investigation of the examination writing given in this audit is expected to help in exploration and merging the current research endeavors and inferring an examination plan for future advancements

A Framework for Estimating the Applicability of GAs for Real‐World Optimization Problems

This paper introduces a methodology for estimating the applicability of a particular Genetic Algorithm (GA) configuration for an arbitrary optimization problem based on run-time data. GAs are increasingly employed to solve complex real-world optimization problems featuring ill-behaved search spaces (e.g., non-continuous, non-convex, non-differentiable) for which traditional algorithms fail. The quality of the optimal solution (i.e., the fitness value of the global optimum) is typically unknown in a real-world problem, making it hard to assess the absolute performance of an algorithm which is being applied to that problem. In other words, with a solution provided by a GA run, there generally lacks a method or a theory to measure how good the solution is. Although many researchers applying GAs have provided experimental results showing their successful applications, those are merely averaged-out, \emph{ad hoc} results. The results cannot represent nor guarantee the usability of the best solutions obtained from a single GA run since the solutions can be very different for each run. Therefore, it is desirable to provide a formalized measurement to estimate the applicability of GAs to real-world problems. This work extends our earlier work on the convergence rate, and proposes an evaluation metric to quantify the applicability of GAs. Through this metric, a degree of convergence can be obtained after each GA run so that researchers and practitioners are able to obtain certain information about the relation between the best solution and all of the feasible solutions. To support the proposed evaluation metric, a series of theorems are formulated from the theory of matrices. Moreover, several experiments are conducted to validate the metric