Anoj Kumar

An approach for mutation testing using elitist genetic algorithm

Mutation Testing is used as fault-based testing to overcome limitations of other testing approaches but it is recognized as expensive process. In mutation testing, a good test case is one that kills one or more mutants, by producing different mutant output from the original program. Evolutionary algorithms have been proved its suitability for reducing the cost of data generation in different testing methodologies. In order to reduce the cost of mutation testing, efficient test cases are generated that reveal faults and kill mutants. In this paper, we develop a new strategy for generating efficient test input data in the context of mutation testing.

Notice of Retraction Generation of efficient test data using path selection strategy with elitist GA in regression testing

Regression testing is an expensive and frequently executed maintenance process used to revalidate modified software. Various problems are associated with regression testing such as regression test selection problem, coverage identification problem, test case execution problem, test case maintenance problem etc. In test selection problem, appropriate and effective test data is to be selected from the input domain of test data. One more problem may arise, when tester has to select the modified paths from the set of modified path for test case execution i.e. path selection problem. To overcome these problems, this paper presents a combined approach by which the stated problems are resolved in effective manner. By this approach, tester can identify the appropriate paths for test case execution and also generate efficient test data using elitist version of GA. The proposed approach enables tester to execute the test cases in order to increase their effectiveness to find faults taking minimum efforts. This approach is used in regression testing to choose an appropriate subset of test cases by using elitist GA, among a previously run test suite for a software system, based on the information about the modifications made to the system for enhancement.

Spectrum-Based Fault Localization in Regression Testing

In maintenance, regression testing is used to revalidate modified software which is also an expensive and frequently executed process. In conventional regression testing approach, developers generally re-run all test suite or selectively run a sub-set of existing test cases on the modified version of program. After executing the test cases over program, developer may reveal the regression faults due to changes in code and use these non obsolete test cases from pre-existing test suite to explore and eradicate regression faults. This paper addresses the fundamental limitations of conventional regression testing approach. To overcome these limitations, this paper presents a spectrum-based fault localization strategy by which the stated limitations are resolved in effective manner. Spectrum-based fault localization strategy utilizes various program spectra to identify the behavioral differences between old and new version of the program under test. This comparison is also useful in pinpointing the cause of failures or errors and presence of difference in program spectra may indicate those test cases for which the construction of expected output or oracle or specification is not needed. It is very expensive to compute and verify expected output and some times it is impossible to compute expected outputs for non-trivial programs. This approach is used in regression testing to identify behavioral differences between old and new version of a program.

Particle Swarm Optimization with cognitive avoidance component

This paper introduces cognitive avoidance scheme to the Particle Swarm Optimization algorithm. Random movements of particle influenced by personal best solution and global best solution may encourage to take an unfruitful move. This may delay in convergence towards the optimal solution. With the similar notion as particles own known best position attracts towards it (cognitive attraction), particle may avoid itself from taking moves around its own known worst position (cognitive avoidance). This concept is added to the standard Particle Swarm Optimization algorithm as cognitive avoidance component with an additional coefficient. Experimental results on well known benchmark functions shows considerable improvement in proposed approach.

Optimizing the reliability of communication network using specially designed genetic algorithm

All areas relating to telecommunications, electricity distribution, and gas pipeline require Topological optimization. It also has a major importance in the computer communication industry, when considering network reliability. In this paper, we have used GA with specialized encoding, initialization, local search operators with specially designed crossover operator called alternating crossover [21] to optimize the design of communication network topologies, as this NP-hard problem is often highly constrained so random initialization and standard genetic operators usually generate infeasible networks.

A variant of NSGA-II for solving priority based optimization problems

Many real life problems require optimization of more than one objective functions, these problems are known as multi-objective optimization problems. Although many algorithms are available in literature to solve these types of problems [2, 3, 4, 5, 21] but they treat every objective function equally. Sometimes, on the basis of problem to be optimized, different objective functions can be assigned different priority. In this way, we can reduce the burden of searching in all objective directions, those having higher priority will contribute in search space, those having low priority will be selected by local search from the assigned search space. For dealing such type of problems a modified version of NSGA-II is presented in the paper. This modified algorithm is used to solve a multi-objective problem, which is related to Rotary furnace that is used in small-scale foundry.

A Centrality-measures based Caching Scheme for Content-centric Networking (CCN)

Content-centric networking (CCN) is gradually becoming an alternative approach to the conventional Internet architecture through the distribution of enlightening information (named as content) on the Internet. It is evaluated that the better performance can be achieved by caching is done on a subset of content routers instead of all the routers in the content delivery path. The subset of a content router must be selected in a manner such that maximum cache performance can be achieved. Motivated by this, we propose a Centrality-measures based algorithm (CMBA) for selection of an appropriate content router for caching of the contents. The Centrality-measures are based on the question: ”Who are the most important or central content router in the network for the caching of contents?”. We found that our novel CMBA could improve content cache performance along the content delivery path by using only a subset of available content routers. Our results recommend that our proposed work consistently achieves better caching gain across the multiple network topologies.

Multiobjective differential evolution using homeostasis based mutation for application in software cost estimation

The problem in software cost estimation revolves around accuracy. To improve the accuracy, heuristic/meta-heuristic algorithms have been known to yield better results when it is applied in the domain of software cost estimation. For the sake of accuracy in results, we are still modifying these algorithms. Here we have proposed a new meta-heuristic algorithm based on Differential Evolution (DE) by Homeostasis mutation operator. Software development requires high prediction and low Root Mean Squared Error (RMSE) and mean magnitude relative error(MMRE). The problem in software cost estimation relates to accurate prediction and minimization of RMSE and MMRE, which are used to solve multiobjective optimization. Many versions of DE were proposed, however multi-objective versions where the concept of Pareto optimality is used, are most popular. Pareto-Based Differential Evolution (PBDE) is one of them. Although the performance of this algorithm is very good, its convergence rate can be further improved by minimizing the time complexity of nondominated sorting, and by improving the diversity of solutions. This has been implemented by using efficient nondominated algorithm whose time complexity is better than the previous one and a new mutation scheme is implemented in DE which can provide more diversity among solutions. The proposed variant multiplies the Homeostasis value with one more vector, named the Homeostasis mutation vector, in the existing mutation vector to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escape the situation of stagnation. The performance of the proposed algorithm is evaluated on twelve benchmark test functions (bi-objective and tri-objective) on the Pareto-optimal front. The performance of the proposed algorithm is compared with other state-of-the-art algorithms on five multi-objective evolutionary algorithms (MOEAs). The result verifies that our proposed Homeostasis mutation strategy performs better than other state-of-the-art algorithms. Finally, application of MODE-HBM is applied to solve in terms of Pareto front, representing the trade-off between development RMSE, MMRE, and prediction for COCOMO model.

Software cost estimation using homeostasis mutation based differential evolution

The main concern in the field of software development is estimation of the cost of software at its initial phase of development. The cost estimation usually depends upon the size of the project, which may use lines of code or function points as metrics. In COCOMO, for the accuracy of the cost estimation, cost factors need to be formulated in the individual development environment. In this paper, some new mutation strategies are proposed to improve the accuracy of cost estimation by modifying parameters of COCOMO using Homeostasis mutation based differential evolution(HMBDE). The proposed method adds one more vector named as Homeostasis mutation vector in the existing mutation vector to provide more bandwidth for selecting effective mutant solutions providing a wide search space for probable solution. The proposed approach provides more accurate solutions to guide the evolution. Performance of proposed algorithm is compared with software cost estimation models. The result verifies that our proposed HMBDE performs better than COCOMO based DE and PSO algorithm and other soft computing models.

Differential Evolution Algorithm Using Population-Based Homeostasis Difference Vector

For the last two decades, the differential evolution is considered as one of the powerful nature inspired algorithm which is used to solve real-world problems. DE takes minimum number of function evaluations to reach close to global optimum solution. The performance is very good, but it suffers from the problem of stagnation when tested on multi-modal functions. In this paper, the population-based homeostasis difference vector strategy has been used to improve the performance of differential evolution algorithms. Here we propose two independent difference random vectors named as best difference vector and random difference vector which helps in avoiding stagnation problem of multi-modal functions. The performance of proposed algorithm is compared with other state-of-the-art algorithms on COCO (Comparing Continuous Optimizers) framework. The result verifies that our proposed population-based homeostasis difference vector strategy outperform most of the state-of-the-art DE variants.