Pradeep Tomar

CBS testing requirements and test case process documentation revisited

Component-based software testing is an important capability that supports productivity and quality assurance in component-based software development. The increased size and complexity of software systems has led to the current focus on developing distributed applications that are constructed primarily using components. Thus, the component-based systems require efficient and effective ways to test these systems and need to develop effective techniques for testing various aspects of the components such as reusability, security, dependability and safety. Study on the subject by several researchers indicates that more than fifty percent of the cost of software development is devoted to testing and it results into very high cost for testing complex software. The present paper is aimed at improving component-based system testing while considering two factors: component-based system testing requirement and test case process documentation. Lastly, the paper also discusses the limitations of component-based software testing that hinders component-based development.

Modified development process of component-based software engineering

Component-based software engineering (CBSE) is a branch of software engineering, the priority of which is the separation of concerns in respect of the wide-ranging functionality available throughout a given software system. CBSE emphasizes on building system by reusing high quality configurable software components. This reduces its development cost as well as time-to-market and ensures higher reliability, better maintainability and quality by exploiting reusability. In the traditional approach, when a software system is going to be developed, the implementation has to be done from scratch. With the advent of Object-Oriented Technology (OOT), reusable software components have become an indispensable part of programming knowledge. In addition to those classes and methods included in standard libraries of programming languages, such as the Java API library, many reusable software components are developed by software development organizations specifically for reuse or repackaged from previously developed systems. We propose here a modified development process of CBSE and present our modified development process of CBSE for increasing reusability in different abstraction levels: architecture level, modular design level and framework level. This modified development process of CBSE change the reusability approach into two different approaches composition-based approach and generation-based. Lastly on the basis of these two approaches we divide component reuse into two different processes. First one is process of development of reusable components which is composition-based approach and second one is process of development with reusable components which is based on the generation-based according X model with the benefits of reusable components in programming

New Algorithm for Component Selection to Develop Component-Based Software with X Model

is an approach which is used to enhance the reusability with the development of component-based software from the preexisting software components or with the components which is developed from the scratch. A new algorithm is proposed for component selection by using best-fit strategy and first-fit strategy through X model which is used to develop componentbased software with two approaches likely development for reuse and development with reuse. But when reuse a preexisting software component through the development with reuse, component selection play an important role. Component selection for Component-Based Software Development (CBSD) is very challenging field for researchers and practitioners. This paper also presents the two component selection problem viz. Simple Component Selection Problems (SCSP) and Criteria Component Selection Problem (CCSP). On the basis of these two problems, this paper presents a new optimal solution with new algorithm for optimal component selection from repositories. Lastly, paper summarizes the factors used in algorithm for optimal selection of components with the help of X model repositories to fulfill the requirements of client by using SCSP and CCSP.

Verification & Validation of components with new X Component-Based Model

Software Verification and Validation (V & V) activities check the software against its specifications. Every project must verify and validate the software. This is done by checking that software meets specified requirements by ensuring that the amount of V & V effort is adequate to show each software item is suitable for operational use. In the era of Software Engineering (SE), Component-Based Software Development (CBSD) put new demands on how to ensure the needed functionality and quality of the software with, V & V. In traditional software, V & V could be done in close cooperation with the customer to meet the specific requirements from the customer. When developing software components, or Component-Based Software Systems (CBSS), V & V becomes a bit more complicated. Components developed for reuse, and especially components developed for the open market, have to be more thoroughly specified and verified then most custom software for many reasons. This paper present V & V when developing software components which are helpful in improving the functionality and quality of component and Component-Based System (CBS) by using a new X Component-Based Model.

Automatic goal-oriented test data generation using a Genetic algorithm and simulated annealing

The literature on automatic test case generation has significantly arguments its importance in software testing. The solution to this un-decidable problem can reduce the financial resources spent in testing a software system. In this paper Evolutionary Genetic algorithm and simulated annealing based approach for automatic test case generation is presented. The fitness of target goal is achieved by instrumenting the program using branch distance approach and the generated test cases using genetic algorithm and simulated annealing are evaluated and compared in terms of 1) number of generation needed to reach to the target goal and 2) The time taken to generate test cases.

New method to find the maximum number of faults by analyzing reliability and reusability in Component-Based Software

Component-Based Software Engineering is based on reusability of code. It is an approach through which a customer can get a quality product by paying less amount of money and spending less time to produce in comparison of traditional software engineering process. In CBSE lots of work is present in empirical form regarding SQA. In this paper, we are calculating the maximum number of faults through investigating the reliability and reusability of Component-Based Software. This method is very effective to minimize the cost and time for a CBS by using the Halsted Software Science. This method find the effects on reusability through changes in initial phases of software developing process like requirements analysis, design, coding and simultaneously on reliability of code or program, which would be helpful to find the number of faults.

Approach for automated test data generation for path testing in aspect-oriented programs using genetic algorithm

Aspect-Oriented Programming (AOP) is an emerging programming paradigm that supports implementation of cross-cutting requirements into named program units called aspects. However, these Aspects are hard to deal in many stages of Software Development Life Cycle (SDLC) especially in Aspect-Oriented software testing. Main aim of testing is to find the errors during execution of the program. Error can prevail in any part of the program so this study use Control Flow Graph (CFG) to depicts all path of the program during its execution. Some path of the program executes rarely, so with the help of automated test data generation tester can execute those path because generation of test data for these path is not manually possible. Test data generation process can be automated with the help of various techniques and framework. This work provides review of some of the recent work that has been done in the area of AOP test data generation. Based on those work, this work proposes a approach for generating test data for AOP using Genetic Algorithm (GA).

Prediction of quality using ANN based on Teaching-Learning Optimization in component-based software systems

The primary objective of our research work is to enhance the prediction of the quality of a component‐based software system and to develop an artificial neural network (ANN) model for the system reliability optimization problem. In this paper, we introduced the ANN‐supported Teaching‐Learning Optimization by transforming constraints to objective functions. Artificial neural network techniques are found to be powerful in the modeling software package quality metrics compared with the ancient statistical techniques. Therefore, by using the neural network, the quality characteristics of software components of the proposed work are predicted. A nonlinear differentiable transfer function of ANN used in the proposed approach is hyperbolic tangent sigmoid. A new efficient optimization methodology referred to as the Teaching‐Learning–based Optimization is proposed in this paper to optimize reliability and different cost functions. The weight values of the network are then adjusted consistent with a proposed optimization rule, therefore minimizing the network error. The proposed work is implemented in MATLAB by using the Neural Network Toolbox. The proposed work provides improved performance in terms of sensitivity, precision, specificity, negative predictive value, fall‐out or false positive rate, false discovery rate, accuracy, Matthews correlation coefficient, and rate of convergence.

Improved Meta-Heuristic Technique for Test Case Prioritization

Time is a very critical factor for decision of cost of any software. The cost and validity of software is based on the quality and quantity of the existing test cases. A large number of software testing approaches are available having both advantages and limitations. Original test cases are supposed to be reused and the new test cases have to be supplemented in regression testing of the updated software. For effective and efficient test case prioritization, the techniques like test case prioritization and test case selection were introduced for scheduling test cases and implementing test cases for fulfillment of some particular criteria. Test case prioritization supports the most useful test cases to execute first by making software testing cost-effective and efficiently covering most extreme number of faults in least time. But test case prioritization requires huge time and effort. This paper has proposed an improved meta-heuristic technique (Ant Colony Optimization) algorithm to find the best optimal path by prioritization of test cases.

Test Data Generation Using Optimization Algorithm: An Empirical Evaluation

This paper aims to design an approach for making an efficient fitness function for tests case generation based on distance from the goals. The designed function is given as an input to the genetic algorithm, and the result of the search process using the formed fitness function is evaluated in terms of time and the average number of test cases generated. This paper also investigates the effect of parameter setting such as the size of the initial population on the performance of genetic algorithm using the proposed fitness function. The experimental result shows that the proposed approach is both time and cost efficient in comparison with manual and random testing. It is also found that initial larger population size gives better results in comparison with low initial population.