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Regression testing is an activity of finding bugs in the modified parts of the software and releases the software versions timely to avoid further risks. Retesting of all existing test cases including obsolete and redundant test cases is increasing the cost and efforts of the overall process. In order to reduce this cost and time, optimization algorithms are playing a vital role. This paper focuses on the performance analysis of three recent metaheuristic algorithms: Cuckoo Search, Crow Search Algorithm, and Harris Hawks Optimization to solve the RTCS problem for selecting the test cases. Fault coverage and execution time parameters have been selected for performance evaluation. The experiments are performed and analyzed on standard SIR repository. The results and statistical tests show that Cuckoo Search and Crow Search Algorithm significantly give better results for different parameters of RTCS problem than Harris Hawks Optimization (HHO). The Cuckoo Search outperformed on fault coverage, and Crow Search Algorithm outperformed on time parameter.

Test suite optimization is an ever-demanded approach for regression test cost reduction. Regression testing is conducted to identify any adverse effects of maintenance activity on previously working versions of the software. It consumes almost seventy percent of the overall software development lifecycle budget. Regression test cost reduction is therefore of vital importance. Test suite optimization is the most explored approach to reduce the test suite size to re-execute. This article focuses on test suite optimization as a regression test case selection, which is a proven N-P hard combinatorial optimization problem. The authors have proposed an almost safe regression test case selection approach using a Hybrid Whale Optimization Algorithm and empirically evaluated the same on subject programs retrieved from the Software Artifact Infrastructure Repository with Bat Search and ACO-based regression test case selection approaches. The analyses of the obtained results indicate an improvement in the fault detection ability of the proposed approach over the compared ones with significant reduction in test suite size.

This paper presents a novel method referred as fault coverage-based test suite optimization (FCBTSO) for regression test suite optimization. FCBTSO is proposed based on Harrolds–Gupta–Soffa (HGS) test suite reduction method, and it follows the phenomenon: “learning from mistakes”. We conducted computational experiments on 12 versions of benchmarked programs retrieved from software artefact infrastructure repository and dummy fault matrix test. The performance of the proposed FCBTSO is measured against the traditional test suite reduction methods (Greedy method, Additional Greedy, HGS, and Enhanced HGS) by following the performance measures: fault coverage, execution time and reduced optimized test suite size. Rigorous statistical tests are conducted to determine the performance significance, which indicates that FCBTSO outperforms other approaches implemented with respect to the execution time that includes the execution time of the proposed approach to find the optimized test suite and the execution time of test cases in the optimized test suite.

In modern era, industries demand for innovative and reliable software solutions. To maintain the reliability level of softwares various software reliability growth models were proposed in last four decades. These models performance relies on parameter estimation approaches utilized to find the optimum values of their unknown model parameters. But, developing an approach that provides the perfect optimum parameter for software reliability growth models (SRGMs) has been the issue of concern within the research community over the decades. This paper adopted the Improved Grey Wolf Optimizer (IGWO) for parameter estimation and compares its accuracy with existing approach Grey Wolf Optimizer (GWO) in estimating the optimum parameters for software reliability growth models. GWO imitates the social leadership pyramid and the hunting methods adopted by grey wolves; IGWO was later proposed to resolve the deficiencies observed in GWO for improved performance. Seven real world failure datasets have been utilized to measure and evaluate the performance of the proposed approach against the existing approach. The results indicate that proposed approach (IGWO) outperform the existing one (GWO).

The demand of highly reliable and superior quality open source software's are increasing day by day. This demand force software developers to improvise the reliability of their software's. Authors have proposed several parametric and non-parametric software reliability models but they have their own limitations, like parametric model suffer from unrealistic model assumptions, operating environment condition dependencies. In contrast to parametric, non-parametric models overcome these issues but they are computationally costlier. So, the scope of optimization or development of new reliable model still exists. This paper presents an effective software reliability modeling based on Cuckoo Search optimization, Ensemble Empirical Mode Decomposition and ARIMA modeling of time series to provide more accurate prediction. Extensive experiments on 5 real datasets is conducted and results are collected. The analysis of results indicates the superiority of proposed technique over existing parametric and non-parametric models for open source software's and propriety software's.

Cloud computing is one of the enormously growing and challenging area in the modern era. There is a rapid technological advancement in virtualization technology to achieve most effective features of cloud services like reliability, cost-effective scalable services, availability and on-demand services. Tasks scheduling is one of the most challenging NP-hard problems in cloud systems. In past few decades, various solutions have been proposed to handle task scheduling problem, but the scope of optimization still persists. In this paper, we have proposed an improved harmony search algorithm based approach to solve task scheduling problem to minimize the total completion time measured as makespan and maximize the resource utilization of virtual machine, assuming that tasks are non-preemptive and independent. The proposed algorithm also ensures that no virtual machine is overloaded and its resources are utilized to the maximum level by minimizing makespan. An extensive experiment is designed and simulation is done on heterogeneous computing dataset available from open source. Results indicate the superiority of proposed approach over existing approaches.

Regression testing is a way of catching bugs in new builds and releases to avoid the product risks. Corrective, progressive, retest all and selective regression testing are strategies to perform regression testing. Retesting all existing test cases is one of the most reliable approaches but it is costly in terms of time and effort. This limitation opened a scope to optimize regression testing cost by selecting only a subset of test cases that can detect faults in optimal time and effort. This paper proposes Pareto based Multi-Objective Harmony Search approach for regression test case selection from an existing test suite to achieve some test adequacy criteria. Fault coverage, unique faults covered and algorithm execution time are utilised as performance measures to achieve optimization criteria. The performance evaluation of proposed approach is performed against Bat Search and Cuckoo Search optimization. The results of statistical tests indicate significant improvement over existing approaches.

When an image enters into the digital world, the probability that the image will get tempered also increases. Hence there exists a higher threat to such content (image, audio, and video) to be copied, modified and distributed. In this thesis, LTP (Local Ternary Pattern) along with DCT (Discrete Cosine Transform) have been employed. By using the DCT technique on the JPEG image we are able to provide lossy compression to the image. In this way, the image is compressed such that size of the image reduced enough for storage of content, in this case, a watermark image. Once this has been done, LTP technique is applied to it. The use of LTP will help to embed a watermark even if it is of a heavier size. The original image will be obtained with lesser impacts to it. To show the effectiveness of LTP watermarking, certain attacks like cropping, noise, and compression are applied. As a result, the extracted watermark is tempered. DCT ensures robustness by compressing the image while LTP allows watermark embedding and extraction. DWT and LBP both provide compression and watermarking respectively. But, this paper has briefly compared DCT and DWT techniques to conclude as to why DCT has been chosen. Also, this paper shows reasons as to why LTP is chosen over LBP. Index Terms-Image Compression, Image Watermarking, Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT). Local Ternary Pattern (LTP), Local Binary Pattern (LBP).

The proposed approach put forward an effective estimation of the parameters for software reliability growth models with the help of Krill Herd Algorithm. The Software Reliability Growth model is considered to be incomplete, if the parameters of model are not known and are not validated over failure datasets. Many techniques are present for estimating parameters of model based on numerical estimation, but the scope of quality enhancement still remain open. The proposed Krill Herd Algorithm based approach outclasses the problems of numerical estimation. The proposed work is validated over four real-time datasets.