Arun Kumar Misra

Physics-Inspired Optimization Algorithms: A Survey

Natural phenomenon can be used to solve complex optimization problems with its excellent facts, functions, and phenomenon. In this paper, a survey on physics-based algorithm is done to show how these inspirations led to the solution of well-known optimization problem. The survey is focused on inspirations that are originated from physics, their formulation into solutions, and their evolution with time. Comparative studies of these noble algorithms along with their variety of applications have been done throughout this paper.

Software test effort estimation

Software Testing is an important process of software development that is performed to support and enhance reliability and quality of the software. It consist of estimating testing effort, selecting suitable test team, designing test cases, executing the software with those test cases and examining the results produced by those executions. Studies indicate that more than fifty percent of the cost of software development is devoted to testing, with the percentage for testing critical software being even higher. Unless we can predict the testing effort and find efficient ways to perform effective testing, the percentage of development costs devoted to testing will increase significantly coupled with mismatch in project costing and development schedule. In order to estimate the testing effort, this paper makes an attempt to establish Cognitive Information Complexity Measure (CICM) as an appropriate estimation tool.

Evaluating cognitive complexity measure with Weyuker properties

Cognitive complexity measure is based on cognitive informatics, which in turn helps in comprehending the software characteristics. Weyuker properties must be satisfied by every complexity measure to qualify as a good and comprehensive one. An attempt has been made to evaluate cognitive complexity measure in terms of nine Weyuker properties, through examples. It has been found that eight of nine Weyuker properties have been satisfied by the cognitive weight software complexity measure and hence establishes the cognitive complexity as a well structured one.

A modified cognitive information complexity measure of software

Most discipline of science have their own method of investigation built on foundation that is empirical and verifiable. Cognitive Informatics is no different. Cognitive Informatics is a field that studies internal information processing mechanism of the human brain and its application in software coding and computing. This paper attempts to empirically demonstrate the amount of information contained in software and develops a concept of cognitive information complexity measure based on the information contained in the software. It is found that software with higher cognitive information complexity measure has more information units contained in it. Therefore cognitive information complexity measure can be used to understand the cognitive information complexity and the information coding efficiency of the software.

Estimating software maintenance effort: a neural network approach

Software maintenance forms an essential component of software development. Its planning includes estimation of maintenance effort, duration, personnel and costs. Adequate information regarding size, complexity and maintainability is however often unavailable. In the present work, a Neural Network (NN) based effort estimator is developed using Matlab. A feed forward back- propagation NN employing Bayesian regularization training is selected and trained for one dataset. Various categories of software maintenance cost drivers and their effect on maintenance effort have been analyzed using different combinations of number of hidden layers and hidden neurons etc. The NN is able to successfully model the maintenance effort as the obtained results are well within the previously published error limits

Robustness analysis of cognitive information complexity measure using Weyuker properties

Cognitive information complexity measure is based on cognitive informatics, which helps in comprehending the software characteristics. For any complexity measure to be robust, Weyuker properties must be satisfied to qualify as good and comprehensive one. In this paper, an attempt has also been made to evaluate cognitive information complexity measure in terms of nine Weyuker properties, through examples. It has been found that all the nine properties have been satisfied by cognitive information complexity measure and hence establishes cognitive information complexity measure based on information contained in the software as a robust and well-structured one.

Optimization of the Quine-McCluskey Method for the Minimization of the Boolean Expressions

The basic principle in designing digital circuit hovers around reducing the required hardware thus reducing the cost too. To achieve this, we use Boolean expression that helps in obtaining minimum number of terms and does not contain any redundant pairs. The conventional methods for the minimization of the Boolean expressions are K-Map method and the . The minimized expressions are used to design digital circuits. Since K-Map method gets exceedingly complex when the number of the variable exceed six, hence Quine-McCluskey tabulation method scores over this and is widely used .In the following paper we present optimized Quine- McCluskey method that reduces the run time complexity of the algorithm by proposing an efficient algorithm for determination of Prime Implicants.

Creating Reusable Software Component from Object-Oriented Legacy System through Reverse Engineering

Due to the constant change in technology, lack of standardization, difficulties of changes and absence of distributed architecture, the business value of legacy systems have become weaker. We cannot undermine the importance of legacy systems because some of their functions are too valuable to be discarded and too expensive to reproduce. The software industry and researchers have recently paid more attention towards the component based software development to enhance the productivity and accelerate time to market. Instead of re-implementing the business critical applications with-in time and resource constraints, the best option is Software Reengineering (SRE) with effective design and architecture which can make the system better for reusability and maintainability. The main motive behind the reengineering is integrating the legacy system with emerging technologies. To achieve these goals, we have proposed a systematic and concrete model named as Component Oriented Reverse Engineering (CORE). It aims to identify and develop reusable software components. By using the reverse engineering techniques; we can extract architectural information and services from legacy system and later on convert these services into components

Improved cognitive information complexity measure: a metric that establishes program comprehension effort

Understanding the software system is known as program comprehension and is a cognitive process. This cognitive process is the driving force behind creation of software that is easier to understand i.e. has lower cognitive complexity, because essentially it is the natural intelligence of human brain that describes the comprehensibility of software. The research area carrying out this study is cognitive informatics. This work has developed an improved cognitive information complexity measure (CICM) that is based on the amount of information contained in the software and encompasses all the major parameters that have a bearing on the difficulty of comprehension or cognitive complexity of software. It is also able to establish the relationship between cognitive complexity of a program and time taken to understand the program, thus mapping closely to the comprehension strategy of a person.

A bio inspired algorithm for solving optimization problems

Although a number of nature inspired algorithms exist in literature to solve optimization problems, yet there is always a need of new algorithm which can search for optimum solution in minimum time. This paper proposes a new optimization algorithm for solving optimization problems. Proposed algorithm has been compared with existing algorithm like Genetic Algorithm, Particle swarm Optimization on benchmark functions and experiments prove that proposed algorithm is better in many cases.