Amélioration de la prédiction de la qualité du logiciel par combinaison et adaptation de modèles

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Soft Skills and Software Development: A Reflection from the Software Industry

Psychological theories assert that not everybody is fit for every task, as people have different personality traits and abilities. Often, personality traits are expressed in people’s soft skills. That is, the way people perceive, plan and execute any assigned task is influenced by their set of soft skills. Most of the studies carried out on the human factor in IS concentrate primarily on personality types. Soft skills have been given comparatively little attention by researchers. We review the literature relating to soft skills and the software engineering and information systems domain before describing a study based on 650 job advertisements posted on well-known recruitment sites from a range of geographical locations including, North America, Europe, Asia and Australia. The study makes use of nine defined soft skills to assess the level of demand for each of these skills related to individual job roles within the software industry. This work reports some of the vital statistics from industry about the requirements of soft skills in various roles of software development phases. The work also highlights the variation in the types of skills required for each of the roles. We found that currently although the software industry is paying attention to soft skills up to some extent while hiring but there is a need to further acknowledge the role of these skills in software development. The objective of this paper is to analyze the software industry’s soft skills requirements for various software development positions, such as system analyst, designer, programmer, and tester. We pose two research questions, namely, (1) What soft skills are appropriate to different software development lifecycle roles, and (2) Up to what extend does the software industry consider soft skills when hiring an employee. The study suggests that there is a further need of acknowledgment of the significance of soft skills from employers in software industry

Soft Skills Requirements in Software Development Jobs: A Cross-Cultural Empirical Study

Purpose: Most of the studies carried out on human factor in software development concentrate primarily on personality traits. However, soft skills which largely help in determining personality traits have been given comparatively little attention by researchers. The purpose of this paper is to find out whether employers’ soft skills requirements, as advertised in job postings, within different roles of software development, are similar across different cultures. Design/methodology/approach: The authors review the literature relating to soft skills before describing a study based on 500 job advertisements posted on well-known recruitment sites from a range of geographical locations, including North America, Europe, Asia and Australia. The study makes use of nine defined soft skills to assess the level of demand for each of these skills related to individual job roles within the software industry. Findings: It was found that in the cases of designer, programmer and tester, substantial similarity exists for the requirements of soft skills, whereas only in the case of system analyst is dissimilarity present across different cultures. It was concluded that cultural difference does not have a major impact on the choice of soft skills requirements in hiring new employee in the case of the software development profession. Originality/value: Specific studies concerning soft skills and software development have been sporadic and often incidental, which highlights the originality of this work. Moreover, no concrete work has been reported in the area of soft skills and their demand as a part of job requirement sets in diverse cultures, which increases the value of this paper

A feedback based quality assessment to support open source software evolution: the GRASS case study

Abstrac

A Combination of Compositional Index and Genetic Algorithm for Predicting Transmembrane Helical Segments

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method. Availability: The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

There are two primary ways to save energy within a building: (1) through improving building engineering structures and adopting efficient appliance ownership, and (2) through changing occupants’ energy-consuming behaviors. Unfortunately the second way suffers from many challenges and limitations. Occupant behavior is, indeed, a complex and multi-disciplinary concept depending on several human factors. Although its importance is recognized by the energy management community, it is often oversimplified and naively defined when used to study, analyze or model energy load. This paper aims at promoting the definition of occupant behavior as well as exploring the extent to which the latter is involved in research works, targeting directly or indirectly energy savings. Hence, in this work, we propose an overview of interdisciplinary research approaches that consider occupants’ energy-saving behaviors, while we present the big picture and evaluate how occupant behavior is defined, we also propose a categorization of the major works that consider energy-consuming occupant behavior. Our findings via a literature review methodology, based on a bibliometric study, reveal a growth of the number of research works involving occupant behavior to model load forecasting and household segmentation. We have equally identified a research trend showing an increasing interest in studying how to successfully change occupant behaviors towards energy saving

IFGAN—A Novel Image Fusion Model to Fuse 3D Point Cloud Sensory Data

To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of critical maneuvers in all situations. However, numerous accidents involving autonomous vehicles (AVs) developed by major automobile manufacturers in recent years have been attributed to poor decision making caused by insufficient perception of environmental information. AVs employ diverse sensors in today’s technology-driven settings to gather this information. However, due to technical and natural factors, the data collected by these sensors may be incomplete or ambiguous, leading to misinterpretation by AVs and resulting in fatal accidents. Furthermore, environmental information obtained from multiple sources in the vehicular environment often exhibits multimodal characteristics. To address this limitation, effective preprocessing of raw sensory data becomes essential, involving two crucial tasks: data cleaning and data fusion. In this context, we propose a comprehensive data fusion engine that categorizes various sensory data formats and appropriately merges them to enhance accuracy. Specifically, we suggest a general framework to combine audio, visual, and textual data, building upon our previous research on an innovative hybrid image fusion model that fused multispectral image data. However, this previous model faced challenges when fusing 3D point cloud data and handling large volumes of sensory data. To overcome these challenges, our study introduces a novel image fusion model called Image Fusion Generative Adversarial Network (IFGAN), which incorporates a multi-scale attention mechanism into both the generator and discriminator of a Generative Adversarial Network (GAN). The primary objective of image fusion is to merge complementary data from various perspectives of the same scene to enhance the clarity and detail of the final image. The multi-scale attention mechanism serves two purposes: the first, capturing comprehensive spatial information to enable the generator to focus on foreground and background target information in the sensory data, and the second, constraining the discriminator to concentrate on attention regions rather than the entire input image. Furthermore, the proposed model integrates the color information retention concept from the previously proposed image fusion model. Furthermore, we propose simple and efficient models for extracting salient image features. We evaluate the proposed models using various standard metrics and compare them with existing popular models. The results demonstrate that our proposed image fusion model outperforms the other models in terms of performance