Towards a general framework for evaluating intelligent environments methodologies

Recent studies reveal that there are different methodologies for developing Intelligent Environments. Thus, it has become essential to scrutinize and evaluate the methodologies to increase our understanding of their strengths, weaknesses and features. However, these concerns have not been the target of recent research efforts. This paper presents an evaluation framework for qualitative evaluation of Intelligent Environment methodologies. It is a step towards standardization of current Intelligent Environments methodologies. The framework has been defined through studying, abstracting and unifying best practices from systems engineering. It is based on a generic life cycle model. As an initial validation, we evaluated the User Centred Intelligent Environment Development Process against the proposed framework. We note that this methodology at its current state presents some limitations which will be addressed in future works

Automatic bug triaging techniques using machine learning and stack traces

When a software system crashes, users have the option to report the crash using automated bug tracking systems. These tools capture software crash and failure data (e.g., stack traces, memory dumps, etc.) from end-users. These data are sent in the form of bug (crash) reports to the software development teams to uncover the causes of the crash and provide adequate fixes. The reports are first assessed (usually in a semi-automatic way) by a group of software analysts, known as triagers. Triagers assign priority to the bugs and redirect them to the software development teams in order to provide fixes. The triaging process, however, is usually very challenging. The problem is that many of these reports are caused by similar faults. Studies have shown that one way to improve the bug triaging process is to detect automatically duplicate (or similar) reports. This way, triagers would not need to spend time on reports caused by faults that have already been handled. Another issue is related to the prioritization of bug reports. Triagers often rely on the information provided by the customers (the report submitters) to prioritize bug reports. However, this task can be quite tedious and requires tool support. Next, triagers route the bug report to the responsible development team based on the subsystem, which caused the crash. Since having knowledge of all the subsystems of an ever-evolving industrial system is impractical, having a tool to automatically identify defective subsystems can significantly reduce the manual bug triaging effort. The main goal of this research is to investigate techniques and tools to help triagers process bug reports. We start by studying the effect of the presence of stack traces in analyzing bug reports. Next, we present a framework to help triagers in each step of the bug triaging process. We propose a new and scalable method to automatically detect duplicate bug reports using stack traces and bug report categorical features. We then propose a novel approach for predicting bug severity using stack traces and categorical features, and finally, we discuss a new method for predicting faulty product and component fields of bug reports. We evaluate the effectiveness of our techniques using bug reports from two large open-source systems. Our results show that stack traces and machine learning methods can be used to automate the bug triaging process, and hence increase the productivity of bug triagers, while reducing costs and efforts associated with manual triaging of bug reports

A Systematic Mapping Study on Scrum and Kanban in Software Development

Background: Agile methodologies, such as Scrum and Kanban, have gained significant popularity in software development organizations. However, there is a need to compare and contrast these methodologies to determine their effectiveness and suitability in specific conditions. Objective: The objective of this systematic mapping study is to compare Scrum and Kanban in software development organizations and identify their methodological differences, benefits, drawbacks, and current/future trends. Method: A comprehensive literature review was conducted, analyzing 47 primary studies. Data synthesis and analysis were performed to extract relevant information on the characteristics of Scrum and Kanban. Results: The study identified several methodological differences between Scrum and Kanban, highlighting their unique characteristics and implementation considerations. The study presents a detailed breakdown of the reported differences, benefits, drawbacks, and trends associated with these methodologies. Conclusions: Choosing between Scrum and Kanban depends on the specific needs, context, and goals of the organization. Scrum excels in areas such as path clarity, delivery time, and teamwork, while Kanban offers advantages in flexibility, easy transition, and focus on work. The findings emphasize the importance of understanding requirements, team dynamics, project characteristics, and customer expectations when selecting an agile methodology. This systematic mapping study contributes to the understanding of Scrum and Kanban in software development organizations. By considering the findings, organizations can make informed decisions and optimize their agile practices to enhance productivity, efficiency, and quality in software development projects

Спосіб маршрутизації в інтелектуальних мережах з технологією SDN

Бакалаврська дипломна робота присвячена вирішенню проблеми конструювання трафіку в інтелектуальних мережах за допомогою технології SDN. Запропонований спосіб маршрутизації зорієнтований на використання переважно у транспортних мережах. Описаний програмний продукт створює можливість скорочення матеріальних витрат та аварійних ситуацій, підвищує надійність системи та швидкість конструювання трафіку.The Bachelor's thesis is developed for solving the problem of traffic engineering in intelligent networks, using SDN technology. The proposed routing method is focused on use mainly in transport networks. The described software product creates the possibility of reducing material costs and emergencies, increases system reliability and speed of traffic engineering

Non-functional requirements for locomotives : a South African rail study

M.Ing. (Engineering Management)Abstract: A South African freight rail company aims to become one of the top 5 railway companies in the world. In the 2012/13 financial year, the company set aside over R7.5 billion for the procurement of new locomotives and rolling stock. This is the largest procurement event that the company has ever undertaken (Molefe, 2012; Crompton, et al., 2016). The requirements development process of the locomotives had to be done as detailed as possible so that they can be designed and built for purpose. Errors made in determining requirements can be costly in terms of revenue, time, system performance, reputation and even survival (Beecham, et al., 2005). When a specification for a new locomotive is being developed, the non-functional requirements are not always identified and defined. This can cause reliability issues once the locomotive is commissioned for operation. Railway companies do not use a standardized non-functional requirements classification model for the development of their locomotives. The examined literature indicated that there are numerous non-functional requirement classification models dating as far back as 1978. However, throughout the development and evolution of these classification models, none of them is a “fit all situations”. 84 unique non-functional requirements were identified in literature and these were compared to what is being specified in locomotive specifications. The findings from the literature review along with the specifications from railway companies was then collated into a format that could be used in research. The study undertook a quantitative research approach whereby the research methodology is descriptive and a questionnaire was used as the data collection tool. The questionnaire was administered to employees of a South African freight railway company in order to determine their view regarding which non-functional requirements are being considered by this organisation when it develops locomotives. The findings of the study showed that the most important non-functional requirements are reliability, maintainability, usability, stability, functionality, fault tolerance, efficiency, performance, predictability and testability. These non-functional requirements are also found in industry and also are dominant in literature. In addition, the results showed that these non-functional requirements should not be observed in isolation, other activities such as adherence to maintenance schedules, quality of..