Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach

Context: Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap: Existing works did not take into account the issue of uncertain class labels, which is an important inherent characteristic of the smells detection problem. More precisely, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Unluckily, existing approaches usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. Throwing away and/or disregarding the uncertainty factor could considerably degrade the detection/identification process effectiveness. From a solution approach viewpoint, there is no work in the literature that proposed a method that is able to detect and/or identify code smells while preserving the uncertainty aspect. Objective: The main goal of our research work is to handle the uncertainty factor, issued from human experts, in detecting and/or identifying code smells by proposing an evolutionary approach that is able to deal with anti-patterns classification with uncertain labels. Method: We suggest Bi-ADIPOK, as an effective search-based tool that is capable to tackle the previously mentioned challenge for both detection and identification cases. The proposed method corresponds to an EA (Evolutionary Algorithm) that optimizes a set of detectors encoded as PK-NNs (Possibilistic K-nearest neighbors) based on a bi-level hierarchy, in which the upper level role consists on finding the optimal PK-NNs parameters, while the lower level one is to generate the PK-NNs. A newly fitness function has been proposed fitness function PomAURPC-OVA_dist (Possibilistic modified Area Under Recall Precision Curve One-Versus-All_distance, abbreviated PAURPC_d in this paper). Bi-ADIPOK is able to deal with label uncertainty using some concepts stemming from the Possibility Theory. Furthermore, the PomAURPC-OVA_dist is capable to process the uncertainty issue even with imbalanced data. We notice that Bi-ADIPOK is first built and then validated using a possibilistic base of smell examples that simulates and mimics the subjectivity of software engineers opinions. Results: The statistical analysis of the obtained results on a set of comparative experiments with respect to four relevant state-of-the-art methods shows the merits of our proposal. The obtained detection results demonstrate that, for the uncertain environment, the PomAURPC-OVA_dist of Bi-ADIPOK ranges between 0.902 and 0.932 and its IAC lies between 0.9108 and 0.9407, while for the certain environment, the PomAURPC-OVA_dist lies between 0.928 and 0.955 and the IAC ranges between 0.9477 and 0.9622. Similarly, the identification results, for the uncertain environment, indicate that the PomAURPC-OVA_dist of Bi-ADIPOK varies between 0.8576 and 0.9273 and its IAC is between 0.8693 and 0.9318. For the certain environment, the PomAURPC-OVA_dist lies between 0.8613 and 0.9351 and the IAC values are between 0.8672 and 0.9476. With uncertain data, Bi-ADIPOK can find 35% more code smells than the second best approach (i.e., BLOP). Furthermore, Bi-ADIPOK has succeeded to reduce the number of false alarms (i.e., misclassified smelly instances) by 12%. In addition, our proposed approach can identify 43% more smell types than BLOP and reduces the number of false alarms by 32%. The same results have been obtained for the certain environment, demonstrating Bi-ADIPOK's ability to deal with such environment

On the Use of Artificial Malicious Patterns for Android Malware Detection

International audienceMalware programs currently represent the most serious threat to computer information systems. Despite the performed efforts of researchers in this field, detection tools still have limitations for one main reason. Actually, malware developers usually use obfuscation techniques consisting in a set of transformations that make the code and/or its execution difficult to analyze by hindering both manual and automated inspections. These techniques allow the malware to escape the detection tools, and hence to be seen as a benign program. To solve the obfuscation issue, many researchers have proposed to extract frequent Application Programming Interface (API) call sequences from previously encountered malware programs using pattern mining techniques and hence, build a base of fraudulent behaviors. Based on this process, it is worth mentioning that the performance of the detection process heavily depends on the base of examples of malware behaviors; also called malware patterns. In order to deal with this shortcoming, a dynamic detection method called Artificial Malware-based Detection (AMD) is proposed in this paper. AMD makes use of not only extracted malware patterns but also artificially generated ones. The artificial malware patterns are generated using an evolutionary (genetic) algorithm. The latter evolves a population of API call sequences with the aim to find new malware behaviors following a set of well-defined evolution rules. The artificial fraudulent behaviors are subsequently inserted into the base of examples in order to enrich it with unseen malware patterns. The main motivation behind the proposed AMD approach is to diversify the base of malware examples in order to maximize the detection rate. AMD has been tested on different Android malware data sets and compared against recent prominent works using commonly employed performance metrics. The performance analysis of the obtained results shows the merits of our AMD novel approach

Model refactoring by example: A multi‐objective search based software engineering approach

Declarative rules are frequently used in model refactoring in order to detect refactoring opportunities and to apply the appropriate ones. However, a large number of rules is required to obtain a complete specification of refactoring opportunities. Companies usually have accumulated examples of refactorings from past maintenance experiences. Based on these observations, we consider the model refactoring problem as a multi objective problem by suggesting refactoring sequences that aim to maximize both structural and textual similarity between a given model (the model to be refactored) and a set of poorly designed models in the base of examples (models that have undergone some refactorings) and minimize the structural similarity between a given model and a set of well‐designed models in the base of examples (models that do not need any refactoring). To this end, we use the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) to find a set of representative Pareto optimal solutions that present the best trade‐off between structural and textual similarities of models. The validation results, based on 8 real world models taken from open‐source projects, confirm the effectiveness of our approach, yielding refactoring recommendations with an average correctness of over 80%. In addition, our approach outperforms 5 of the state‐of‐the‐art refactoring approaches.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143783/1/smr1916.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143783/2/smr1916_am.pd

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Traitement de surface et caractérisation de l'adhérence dans les assemblages métal/bio-composite

Structural bonding is a widely used technique that consists in assembling two or more materials using an adhesive. Due to its numerous advantages mainly mass reduction, vibration damping and bond continuity, this bonding technique has found its place among different fields such as transportation, automotive industry and aerospace aviation. Yet the application of this method could sometimes be limited by the low adhesion properties of some materials. Neutral substrates are usually protected by grease, oil, or a chemically modified layer due to oxidation phenomena and environmental pollution. Thus untreated materials have very low surface free energy and limited available areas for bonding. Surface treatment before bonding is a necessary step to generate dipoles on the surface and to improve surface quality. The key parameters considered to ensure a strong metal to metal, composite to composite or metal to composite adhesion are the surface topography, roughness and free energy. The primarily goal of this thesis, is to improve flax composite to galvanized steel bonded assembly adhesion strength. In this regard, the effect of different surface treatments on both materials’ surface quality was studied. Afterwards, single lab shear joint mechanical tests were applied for both composite to composite and steel to steel assemblies in order to identify the surface treatment providing to each material the best adhesion abilities. Mechanical tests revealed that flax composite and galvanized steel specimens treated with sandblasting using a 90° angle, a 5 bar pressure and a 90/150 glace sand granulometry, presented the highest shear adhesion strength. Yet, once exposed to heat and humidity, composite to steel assemblies, which their surfaces were treated using sandblasting, witnesses a significant decrease in their adhesion strength compared to non-treated specimens. Sandblasting was also proven to causing surface defect and sand residues which could eventually result in premature assembly failure.Le collage structural est l’assemblage de deux ou plusieurs matériaux via un adhésif. Cette technique s’impose de plus en plus dans l’industrie grâce à ses nombreux avantages tels que l’amélioration de la performance globale des assemblages, l’optimisation de la définition des structures et la simplification de certains processus industriels. Toutefois, l’utilisation de cette technique peut être limitée par les faibles propriétés d’adhésion des matériaux à l’état brut. A cet effet, le traitement de surface est une étape primordiale afin d’améliorer et augmenter l’aptitude du collage du matériau. L’objectif principal de cette thèse est d’établir une procédure de préparation de surface confèrent à l’acier galvanisé et au composite lin/époxy les meilleures propriétés d’adhésion. Une étude de l’impact des différents traitements mécaniques et/ou chimiques sur la topographie, la rugosité, la mouillabilité et l’énergie libre de surface de ces deux matériaux ainsi que la résistance au cisaillement des collages acier/acier, composite/composite et acier/composite a été établie. Cette étude a révélé qu’un traitement mécanique par sablage conférait à l’acier et au composite une aptitude du collage plus importante que d’autres traitements tels que le polissage, le tissu d’arrachage et les primaires de surface. Néanmoins, ce traitement engendrait quelques effets indésirables tels que l’endommagement des fibres de lin, la création des microfissures, la présence des résidus de sable et la fragilisation de la tenue de l’assemblage acier/composite à l’humidité

Surface treatment and adhesion characterization in metal/biocomposite assemblies

Etudier l'effet de différents traitements de surface mécaniques et/ou chimiques sur les qualités de surface et les propriétés d'adhésion de l'acier galvanisé et le composite lin-époxy.Study the effect of different surface treatments on the galvanized steel and the flax composite surface qualities and adhesion properties

Monolingual and cross-lingual information retrieval in cultural microblog at CLEF 2018

For CLEF 2018, we focus on cultural microblog search. The aim of this work is to find relevant microcritics in a monolingual and cross lingual context about films. This task is challenging due to the short length of the query and of the documents. For the monolingual context we propose to expand the query using a probalistic weighting scheme. For the French-english cross language task, we used a state of the art approach based on query transation.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Billingual formal concept analysis for cross-language information retrieval

We propose and evaluate a Cross-language Information Retrieval model (CLIR) based on the extraction and the translation of Formal Concepts avoiding queries and/or documents translation. The contribution of this work is the unified formal framework that integrates Formal Concept Analysis (FCA) and information retrieval for effective CLIR. The model is indexing bilingual documents using bilingual Formal Concepts extracted by a FCA. Moreover, the use of noun phrases, in addition to keywords, as indexes is studied. We use two comparable collections: an Italian-French collection and an English-French collection. To evaluate our model, we use three Information Retrieval models: TF. IDF, BM25 and Language Model. Finally, we study the query expansion results. Our main finding suggests that Formal Concept Analysis is effective to align Formal Concepts from different languages. Results indicate that our model performances are comparable to a words translation approach and better than a words embedding approach.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Recent advances in evolutionary multi-objective optimization

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization