Abstract Software refactoring solutions aim at mitigating the negative effects of code and design smells on the overall software quality. Many efforts have been exerted to improve the software refactoring process. However, most of these efforts, despite their contributions, overlooked the side effects of the identified refactoring opportunities that may lead to new smells that will go unnoticed. This paper addresses the side effects of software refactoring and proposes sound solutions for handling them. Unlike current practices in software maintenance, we recommend three different approaches to handle the refactoring side effects. In the first approach, called the baseline, we opt to ignore the smells, caused by refactoring, while executing the identified refactoring decisions. In the second one, refactoring decisions are continually updated to fix all smells caused by side effects. In the last approach, only a subset of these smells is appended to the original smell sequence during the execution of the refactoring decisions. Thanks to the proposed approaches, optimal refactoring decisions are identified using a multi‐objective (MO) optimization algorithm commonly known as the MO covariance matrix adaptation evolution strategy (MO‐CMA‐ES). Experiment results corroborate our assumptions and show the superiority of the second approach over the other ones.

Abstract Software smells indicate design or code issues that might degrade the evolution and maintenance of software systems. Detecting and identifying these issues are challenging tasks. This paper explores, identifies, and analyzes the existing software smell detection techniques at design and code levels. We carried out a systematic literature review (SLR) to identify and collect 145 primary studies related to smell detection in software design and code. Based on these studies, we address several questions related to the analysis of the existing smell detection techniques in terms of abstraction level (design or code), targeted smells, used metrics, implementation, and validation. Our analysis identified several detection techniques categories. We observed that 57% of the studies did not use any performance measures, 41% of them omitted details on the targeted programing language, and the detection techniques were not validated in 14% of these studies. With respect to the abstraction level, only 18% of the studies addressed bad smell detection at the design level. This low coverage urges for more focus on bad smell detection at the design level to handle them at early stages. Finally, our SLR brings to the attention of the research community several opportunities for future research.

Malware and malicious code do not only incur considerable costs and losses but impact negatively the reputation of the targeted organisations. Malware developers, hackers, and information security specialists are continuously improving their strategies to defeat each other. Unfortunately, there is no one‐size‐fits‐all solution to detect and eradicate any malware. This situation is aggravated more by the undetected vulnerabilities that usually impair computer software and internet tools. Such vulnerabilities will remain undetected until fully exploited by malware developers, which will eventually cause considerable financial and reputation losses. In this paper, we propose a novel scheme to detect and classify malware using only image representations of the malware binaries. Highly discriminative features of the malware category and structure are extracted in a compact subspace using principal component analysis. Then, an optimised support vector machine model classifies the extracted features into malware categories. Unlike existing classification models, our solution requires simple algebraic dot products to classify malware based on representative digital images. To assess its performance, publicly‐available image datasets, Malimg , Ember and BIG 2015 , are considered. Our performance analysis indicates that their classifier outperforms state‐of‐the‐art models and attains classification accuracies of 0.998, 0.911, and 0.997 using Malimg , Ember and BIG 2015 malware datasets, respectively.

A mathematical framework for conventional commitment schemes is proposed. Digital contract‐signing protocols represent an important application of the proposed framework, where usually two mistrusted parties wish to exchange their commitments in a fair way. Building on a variant of the proposed framework, an efficient contract‐signing protocol over the Internet is developed. The latter protocol is ‘optimistic, fairness and abuse‐free’. Detailed security and performance analyses are provided. The performance analysis reveals an important computational aspect of the proposed protocol which requires only ‘four rounds’ to complete unlike existing protocols. Moreover, the communication and computational costs are relatively small. Given these theoretical and practical features, the proposed contract‐signing protocol is not only of theoretical interest, but it also enjoys practical merits which make it very suitable for electronic transactions requiring online signature exchange.

Perceptual hashing provides compact and efficient representations for image retrieval, authentication and tamper detection applications. However, most of existing perceptual hashing algorithms are designed for gray-level images and, therefore, color correlation and interaction are simply ignored. In this paper, we propose a novel perceptual hashing for color images using the quaternion singular value decomposition (Q-SVD). In this algorithm, color images are processed through randomized dimensionality reduction which results in secure and robust hashing codes. The motivation behind our work is twofold: 1) a compact representation of color images where the red, green and blue (RGB) components are handled as a single entity using hypercomplex representations and 2) the ability of Q-SVD decomposition to provide the best low-rank approximation of quaternion matrices in the sense of Frobenius norm. Possible geometric attacks are properly modeled as an independent and identically-distributed hypercomplex noise on the singular vectors. Such modeling simplifies the hash code detector design. Finally, the hashing robustness against geometric attacks is evaluated over a large set of standard test images using the receiver operating characteristics analysis. The proposed scheme outperforms SVD-based hashing algorithms in terms of lower miss and false alarm probabilities by orders of magnitude.

This paper presents a new perceptual image hashing approach that exploits the image color information using hypercomplex (quaternionic) representations. Unlike grayscale-based techniques, the proposed approach preserves the color interaction between the image components that have a significant contribution in the generated perceptual image hash codes. Having a robust image hash function optimizes a wide range of applications including content-based retrieval, image authentication, and image watermarking. Initially, the input color image is processed in a “holistic” manner using the hypercomplex representation where the red, green and blue (RGB) components are handled as a single entity. Then, non-overlapping 8 × 8 image blocks are processed using the Quaternion Fourier transform (QFT). Binary image hash codes are generated by comparing the block mean frequency energy to the global mean frequency energy. For retrieval purposes, the Hamming distance (HD) is used as the comparison metric to retrieve perceptually similar images. The performance of the proposed perceptual hashing for color image is compared to that based on the conventional complex Fourier transform (CFT). Simulation results clearly indicate the superior retrieval performance of the proposed QFT-based perceptual hashing technique in term of HD values of intra-and inter-class image samples. Moreover, the performance improvement of the QFT-based technique is achieved at a computational complexity similar to the CFT-based scheme.

Digital multimedia content (especially audio) is becoming a major part of the average computer user experience. Large digital audio collections of music, audio and sound effects are also used by the entertainment, music, movie and animation industries. Therefore, the need for identification and management of audio content grows proportionally to the increasing widespread availability of such media virtually "any time and any where" over the Internet. In this paper, we propose a novel framework for robust perceptual hashing of audio content using balanced multiwavelets (BMW). The framework for generating robust perceptual hash values (or fingerprints) is described. The generated hash values are used for identifying, searching, and retrieving audio content from large audio databases. Furthermore, we illustrate, through extensive computer simulation, the robustness of the proposed framework to efficiently represent audio content and withstand several signal processing attacks and manipulations

Following a recently introduced perceptual model for balanced multiwavelets, we outline, in this paper, an extension of our previous work and propose a new perceptual model for scalar wavelets. The proposed model is derived using multiresolution domain extensions of our previous scheme. Unlike existing models, the proposed one depends only on the image activity and not the filter sets used by the transform. The perceptual redundancy, present in the image, is efficiently quantified through a just-noticeable distortion (JND) profile. In this model, a visibility threshold of distortion is assigned to each wavelet subband coefficient Therefore, perceptually insignificant subband components can be clearly discriminated from perceptually significant ones. For instance, this discrimination can be constructively used to achieve the imperceptibility requirement often encountered in watermarking and data hiding applications. Furthermore, we illustrate, through simulation, the ability of the proposed model to efficiently capture the salient features of the underlying image regardless of the wavelet filters being used.

Digital multimedia content (especially audio) is becoming a major part of the average computer user experience. Large digital audio collections of music, audio and sound effects are also used by the entertainment, music, movie and animation industries. Therefore, the need for identification and management of audio content grows proportionally to the increasing widespread availability of such media virtually “any time and any where” over the Internet. In this paper, we propose a novel framework for musical content fingerprinting using balanced multiwavelets (BMW). The framework for generating robust perceptual fingerprint (or hash) values is described. The generated fingerprints are used for identifying, searching, and retrieving audio content from large digital music databases. Furthermore, we illustrate, through extensive computer simulation, the robustness of the proposed framework to efficiently represent musical content and withstand several signal processing attacks and manipulations.

In this paper, we describe a two-step variance-adaptive method for image denoising based on a statistical model of the coefficients of balanced multiwavelet transform. The model is derived in a statistical framework from a recent successful scheme developed in the seemingly unrelated front of lossy image compression. Clusters of multiwavelet coefficients are modeled as zero-mean Gaussian random variables with high local correlation. In the adopted framework, we use marginal prior distribution on the variances of the multiwavelet coefficients. Then, estimates of the local variances are used to restore the noisy multiwavelet coefficients based on a minimum mean square error estimation (MMSE) procedure. Experimental results, using images contaminated with additive white Gaussian noise, show that the proposed method outperforms most of the denoising schemes reported in the literature. In this paper, the performance comparison is restricted to non-redundant multiresolution representations.

The emergence of digital multimedia and the proliferation of its use have raised major concerns about the protection of intellectual property. In response to these concerns, digital watermarks have emerged as a possible solution for protecting the intellectual property of digital content. In this paper, we derive estimates of data-hiding capacity of balanced multiwavelet transforms. This class of transforms, relatively new, has useful properties for image processing applications as shown in this paper. Furthermore, we investigate the relevance of two closely related statistical models, developed for scalar wavelets, for modeling the statistics of balanced multiwavelet transform coefficients. Finally, we present performance results of a spread spectrum watermarking system that is based on this new transform.

In ultrasonic nondestructive evaluation (NDE) of materials, pulse echo measurements are masked by the characteristics of the measuring instruments, the propagation paths taken by the ultrasonic pulses, and are corrupted by additive noise. Deconvolution operation seeks to undo these masking effects and extract the defect impulse response which is essential for identification. We show that the use of higher-order statistics (HOS)-based deconvolution methods is more suitable to unravel the aforementioned effects. Synthetic and real ultrasonic data obtained from artificial defects is used to show the improved performance of the proposed technique over conventional ones, based on second-order statistics (SOS), commonly used in ultrasonic NDE.