Alain Abran

École de Technologie Supérieure · Software Engineering

Predictive maintenance contributes to Industry 4.0, as it enables a decrease in maintenance costs and downtime while aiming to increase production and return on investment. Despite the increasing utilization of machine learning techniques in predictive maintenance in industrial systems over the past few years, several challenges remain to be addres...

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This paper introduces a novel approach utilizing hardware honeypots as an additional defensive l...

Embedded systems omnipresent in everyday life and industry are mainly composed of hardware and software that must comply with a number of standards and regulations. However, there is no consensus on the quality characteristics and sub-characteristics of embedded software. This paper presents the steps for modeling an operational quality model for e...

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This study introduces a novel approach utilizing hardware honeypots as an additional defensive l...

Software maintenance is a challenging and laborious software management activity, especially for open-source software. The bugs reports of such software allow tracking maintenance activities and were used in several empirical studies to better predict the bug resolution effort. These reports are known for their large size and contain nonrelevant in...

The recurrent load shedding crisis in South Africa has highlighted the need to accurately predict electricity consumption for residential buildings. This has significant ramifications for daily life and economic productivity. To address this challenge, this study leverages machine learning models to predict the hourly energy consumption of resident...

This study empirically evaluates the functionality coverage of 18 mobile applications (apps) for Postnatal care including a recently developed app in Morocco "Mamma&Baby". This evaluation is based on a comparison of the COSMIC _ISO 19,761 functional size of these apps with the score obtained in a previous evaluation based on functions extraction th...

Background The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoper...

Abstract Functional size measurement (FSM) provides a reliable and objective way to measure productivity and estimate the effort required for software activities. FSM automation, compared to manual measurement, enables to reduce errors, increase consistency, and improve the efficiency of the measurement process. This paper presents a COSMIC-based...

Functional size measurement (FSM) provides a reliable and objective way to measure productivity and estimate the effort required for software activities. FSM automation, compared to manual measurement, enables to reduce errors, increase consistency, and improve the efficiency of the measurement process. This paper presents a COSMIC-based automated...

This paper presents an analysis of the 2022 edition of the 'Software Estimation Challenge' organized by the COSMIC Group. The challenge is based on best practices in software effort estimation, including the use of the COSMIC-ISO 19761 standard for sizing software requirements and the early sizing of software functional and non-functional requireme...

The development of Internet of Things (IoT) systems in university courses encourages students to use multiple skills. Hence, the importance of applying teaching methods like Project Based Learning (PBL) in the development of these kinds of systems for integrating hardware and software components while facing numerous real-life problems. This study...

To professionally plan and manage the development and evolution of the Internet of Things (IoT), researchers have proposed several IoT performance measurement solutions. IoT performance measurement solutions can be very valuable for managing the development and evolution of IoT systems, as they provide insights into performance issues, resource opt...

The Internet of Things (IoT) touches almost every aspect of modern society and has changed the way people live, work, travel and, do business. Because of its importance, it is essential to ensure that an IoT system is performing well, as desired and expected, and that this can be assessed and managed with an adequate set of IoT performance metrics....

Choosing the appropriate missing data (MD) imputation technique for a given software development effort estimation (SDEE) technique is not a trivial task. In fact, the impact of MD imputation on the estimation output depends on the dataset and the SDEE technique used, and there is no best imputation technique in all contexts. Thus, an attractive so...

Software maintenance activities are considered as the most expensive ones within the software lifecycle. Software engineering researchers have strived to improve maintenance effort estimation (MEE) of Open Source Software (OSS) through many empirical studies for maintenance effort estimation in open source software (O-MEE). This study objective is...

Context Software development effort estimation (SDEE) is one of the most challenging aspects in project management. The presence of missing data (MD) in software attributes makes SDEE even more complex. K‐nearest neighbors imputation (KNNI) has been widely used in SDEE to deal with the MD issue. However, KNNI, in its classical process, has low tole...

Aim/Purpose: This study identifies the scale types and measurement units used in the measurement of enterprise architecture (EA) and analyzes the admissibility of the mathematical operations used. Background: The majority of measurement solutions proposed in the EA literature are based on researchers’ opinions and many with limited empirical valida...

Software testing (ST) is one of the most important software development life cycle (SDLC) phases and ST effort is often expressed as a percentage of SDLC effort. Unfortunately, in the literature ST effort percentage ranges from 10% to 60%. In the literature most of the machine learning algorithms and metaheuristics for optimizing them have looked a...

BACKGROUND The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoper...

Software maintenance of Open Source Software (OSS) has gained more attention in recent years and facilitated by the help of the Internet. Since volunteers in OSS do not record the effort of their contribution in maintenance tasks, researchers have to indirectly estimate the maintenance effort of such software. A review of the published OSS-MEE mode...

E-government portals are now playing an important role in facilitating citizens' lives by executing services at any time and from any location. Such a way of providing services results in great benefits for citizens and agencies, particularly within the COVID-19 pandemic context. Over the past few years, a number of best practices have been identif...

Functional size measurement (FSM) is an important basis for measuring productivity and estimating the effort required for software activities. Automating FSM can be very valuable for organizations with a large number of projects to measure in a very short time, and there are several issues related to manual FSM, including measurement errors due to...

Background Software maintenance is known as a laborious activity in the software lifecycle and often considered more expensive than other activities. Open-source software (OSS) has gained considerable acceptance in the industry recently, and the maintenance effort estimation (MEE) of such software has emerged as an important research topic. In this...

The literature on enterprise architecture (EA) proposes several measurement solutions to demonstrate the benefits expected by aligning IT initiatives with business objectives. This study presents the first evaluation of these EA measurement solutions by applying a metrology-coverage evaluation method based on evaluation theory, metrology guidelines...

To help practitioners and researchers choose the most suitable predictors when selecting from existing Software Product Maintainability Prediction (SPMP) models or designing new ones, a literature review of empirical studies on SPMP identified a large number of metrics or factors used as predictors of maintainability. However, there is a redundancy...

This paper reports on a quantitative analysis of the informational significance of SWEBOK knowledge areas in the curriculum guidelines developed as IEEE/ACM recommendations for educational programs in software engineering. The analysis uses a representation of the hierarchical structure of educational content in the form of an oriented bipartite hy...

Open-source software are very used nowadays in the industry, and the performance of the estimation of their maintenance effort becomes an interesting research topic. In this context, researchers have conducted many open-source software maintenance effort estimation (O-MEE) studies based on statistical and machine learning (ML) techniques for better...

The IEEE 2430 measurement design fails primary school mathematics and produces numerical noise rather than a number with metrological properties required in engineering. This article presents an alternative approach to nonfunctional requirement sizing utilizing the COSMIC ISO 19761 method.

A number of measurement solutions have been proposed to manage the development of Enterprise Architectures (EA) but this body of knowledge has not been analyzed to identify any strengths or weaknesses from a measurement perspective. Adopting a systematic literature review (SLR) approach this research identified 23 primary studies on EA measurement...

Software development effort estimation (SDEE) remains as the principal activity in software project management planning. Over the past four decades, several methods have been proposed to estimate the effort required to develop a software system, including more recently machine learning (ML) techniques. Because ML performance accuracy depends on the...

While industrial firms realize the importance of Industry 4.0, many have not yet started implementing the technologies required to harvest the benefits. To enable the adoption of artificial intelligence applications for Industry 4.0 and to address the gap between advances in technologies and their adoption in industry, this paper presents a team-ba...

Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloud-based technologies, such as the Internet of Things. With increasing industry adoption and migration of traditional computing services to the cloud, one of the main challenges in cybersecurity is to provide...

Information technologies (IT) architecture and infrastructure is a significant cost item, especially for enterprises with complex production infrastructure and equipment that require automated and digital devices to collect and process primary data on technological and production processes. Most investment models for enterprise-wide development pro...

This article presents an empirical investigation of the effects of chaotic maps on the performance of metaheuristics. Particle Swarm Optimization and Simulated Annealing are modified to use chaotic maps instead of the traditional pseudorandom number generators and then compared on five common benchmark functions using nonparametric null hypothesis...

For software organizations that rely on Open Source Software (OSS) to develop customer solutions and products, it is essential to accurately estimate how long it will take to deliver the expected functionalities. While OSS is supported by government policies around the world, most of the research on software project estimation has focused on conven...

Data mining (DM) consists in analysing a set of observations to find unsuspected relationships and then summarising the data in new ways that are both understandable and useful. It has become widely used in various medical fields including breast cancer (BC), which is the most common cancer and the leading cause of death among women worldwide. BC d...

Software engineering, a fairly recent engineering discipline, is still evolving without a wide consensus on a body of fundamental principles as in traditional engineering fields with their own long-established principles originating from physics, chemistry and mathematics. This paper reports on a systematic mapping study (SMS) that identified 30 pa...

Software project estimation is important for allocating resources and planning a reasonable work schedule. Estimation models are typically built using data from completed projects. While organizations have their historical data repositories, it is difficult to obtain their collaboration due to privacy and competitive concerns. To overcome the issue...

Functional size has been used in software engineering for more than 40 years. When measured early in the software development life cycle, it can serve as direct input for effort estimation. The COSMIC Functional Size Measurement (FSM) method developed by the Common Software Measurement Consortium (COSMIC) is the latest ISO-compliant functional sizi...

The prediction of software maintainability has emerged as an important research topic to address industry expectations for reducing costs, in particular maintenance costs. In the last decades, many studies have used single techniques to predict software maintainability but there is no agreement as to which technique can achieve the best prediction....

Maintaining software once implemented on the end-user side is laborious and, over its lifetime, is most often considerably more expensive than the initial software development. The prediction of software maintainability has emerged as an important research topic to address industry expectations for reducing costs, in particular, maintenance costs....

This article presents an analysis of the bat algorithm (BA) based on elementary mathematical analysis and statistical comparisons of the first hitting time performance metric distributions obtained on a test set comprising five carefully selected objective functions. The findings show that the BA is not an original contribution to the metaheuristic...

Background: In practice, the developers focus is on early identification of the functional requirements (FR) allocated to software, while the system non-functional requirements (NFRs) are left to be specified and detailed much later in the development lifecycle. Aim: A standards-based model of system performance NFRs for early identification and m...

Missing data is a serious issue in software engineering because it can lead to information loss and bias in data analysis. Several imputation techniques have been proposed to deal with both numerical and categorical missing data. However, most of those techniques used is simple reuse techniques originally designed for numerical data, which is a pro...

This paper presents an exploratory study that applies three data analysis techniques: statistical analysis, data clustering, and visualization conducted to the ISBSG R12 data set. Both SPSS and RapidMiner are used to conduct the analysis. While statistical analysis main advantage is the summarization of data, the overall behavior of the data is los...

This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on artificial intelligence using Bayesian Networks, Infl...

Standards and best practices for software quality guide on handling each quality characteristic individually, but not when two or more characteristics come into conflict such as security and usability. The objectives of this paper are twofold: (a) to argue on the importance of handling the conflicts between quality characteristics in general; (b) t...

Literature on Enterprise Architecture (EA) report that EA is an emerging discipline with an increasing attention from both academia and industry. However, the literature report on some challenges in EA research. For instance, EA modelling and EA measurement. In this paper, we aim to assist the EA community to overcome the challenges found in EA mea...

This paper presents an empirical evaluation of the COSMIC Function Points method (e.g., ISO 19761) through measuring the functional size of 33 prenatal mobile Personal Health Records (mPHRs) apps. This evaluation compares the functional size of each mobile app measured using the COSMIC method to the score of the app obtained in a previous evaluatio...

A systematic mapping study (SMS) of proposed EA measurement solutions was undertaken to provide an in-depth understanding of the claimed achievements and limitations in evidence-based research of enterprise architecture (EA). This SMS reports on 22 primary studies on EA measurement solutions published up to the end of 2018. The primary studies were...

Background: Software product maintainability prediction (SPMP) is an important task to control software maintenance activity, and many SPMP techniques for improving software maintainability have been proposed. In this study, we performed a systematic mapping and review on SPMP studies to analyze and summarize the empirical evidence on the predictio...

This paper presents an exploratory study that applies three data analysis techniques: statistical analysis, data clustering, and visualization conducted to the ISBSG R12 data set. Both SPSS and RapidMiner are used to conduct the analysis. While statistical analysis main advantage is the summarization of data, the overall behavior of the data is los...

Analogy‐based estimation is one of the most widely used techniques for effort prediction in software engineering. However, existing analogy‐based techniques suffer from an inability to correctly handle nonquantitative data. To deal with this limitation, a new technique called 2FA‐kprototypes was proposed and evaluated. 2FA‐kprototypes is based on t...

An empirical evaluation of the Common Software Measurement International Consortium (COSMIC) method has been conducted in this study, by measuring the functional size of 17 mobile Personal Health Records (mPHRs) for pregnancy monitoring. The aim of this evaluation is to compare the functional size of each app measured using the COSMIC method to the...

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise an...

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise an...

Problem: Online higher education (OHE) failure rates reach 40% worldwide. Prediction of student performance at early stages of the course calendar has been proposed as strategy to prevent student failure. Objective: To investigate the application of genetic programming (GP) to predict the final grades (FGs) of online students using grades from an e...

Usability and user experience (U&UX) as important components of software quality are now more critical than ever for mobile app store success. Usability experts use different protocols to evaluate the usability of mobile apps while app store user reviews also produce valuable related information. Our research study proposes a measurement design to...

Accurate estimation of software development effort estimation (SDEE) is fundamental for efficient management of software development projects as it assists software managers to efficiently manage their human resources. Over the last four decades, while software engineering researchers have used several effort estimation techniques, including those...

IT 업계에서 소프트웨어 메인터넌스(Maintenance)란 기획이나 개발에 비해 상대적으로 저평가되고 있는 것이 현실이다. 이는 메인터넌스가 유지보수라는 인식이 강하기 때문이다. 그러나 소프트웨어의 유지보수는 하드웨어와는 다른 차원을 갖고 있다. 즉 새로운 기능이 추가되거나 소스코드의 품질을 향상시키는 경우가 대부분이기 때문이다. 이 책은 소프트웨어 유지개선을 관리적 관점에서 접근했다는 데에도 의의가 있지만 이 분야의 권위 있는 저자들이 풍부한 현장 사례들을 바탕으로 집필했다는 점에서 더 큰 가치가 있다. 그 만큼 활용 범위가 넓고 문제 해결능력이 탁월하다는 방증이다. 이 책은 소프트웨어 유지개선 관리 도메인을 탐...

Abstract BACKGROUND: The ISBSG data repository contains software project data collected from various organizations around the world. PROBLEM: Software engineering data sets, such as the ISBSG repository, typically contain a large number of missing data, which considerably reduces the number of data points available for building estimation models....

Context Software maintenance (SM) has to be planned, which involves SM effort prediction. One type of SM is enhancement, where new functionality is added or existing functionality changed or deleted. Objective Analyze the prediction accuracy of two types of support vector regression (ε-SVR and ʋ-SVR) when applied to predict software enhancement ef...