Physical Sciences and Mathematics Commons^™

Automatically Classifying Non-Functional Requirements With Feature Extraction And Supervised Machine Learning Techniques, Mahtab Ezzatikarami

Electronic Thesis and Dissertation Repository

Abstract. Context and Motivation: Non-functional requirements (NFRs) of a system need to be classified into different types such as usability, performance, etc. This would enable stakeholders to ensure the completeness of their work by extracting specific NFRs related to their expertise. Question/Problem: Because of the size and complexity of requirement specification documents, the manual classification of NFRs is time-consuming, labour-intensive, and error-prone. We thus need an automated solution that can provide a highly accurate and efficient categorization of NFRs. Principal ideas/results: In this investigation, using natural language processing and supervised machine learning (SML) techniques, we investigate with feature extraction techniques …

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …

Automated Anomaly Detection And Localization System For A Microservices Based Cloud System, Priyanka Prakash Naikade

Electronic Thesis and Dissertation Repository

Context: With an increasing number of applications running on a microservices-based cloud system (such as AWS, GCP, IBM Cloud), it is challenging for the cloud providers to offer uninterrupted services with guaranteed Quality of Service (QoS) factors. Problem Statement: Existing monitoring frameworks often do not detect critical defects among a large volume of issues generated, thus affecting recovery response times and usage of maintenance human resource. Also, manually tracing the root causes of the issues requires a significant amount of time. Objective: The objective of this work is to: (i) detect performance anomalies, in real-time, through monitoring KPIs (Key Performance …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

What Malaysian Software Students Think About Testing?, Luiz Fernando Capretz, Shuib Basri, Maythem Adili, Aamir Amin

Electrical and Computer Engineering Publications

Software testing is one of the crucial supporting processes of software life cycle. Unfortunately for the software industry, the role is stigmatized, partly due to misperception and partly due to treatment of the role in the software industry. The present study aims to analyse this situation to explore what inhibit an individual from taking up a software testing career. In order to investigate this issue, we surveyed 82 senior students taking a degree in information technology, information and communication technology, and computer science at two Malaysian universities. The subjects were asked the PROs and CONs of taking up a career …

Identifying External Cross-References Using Natural Language Processing (Nlp), Elham Rahmani

Electronic Thesis and Dissertation Repository

[Context and motivation] Software engineers build systems that need to be compliant with relevant regulations. These regulations are stated in authoritative documents from which regulatory requirements need to be elicited. Project contract contains cross-references to these regulatory requirements in external documents. [Problem] Exploring and identifying the regulatory requirements in voluminous textual data is enormously time consuming, and hence costly, and error-prone in sizable software projects. [Principal idea and novelty] We use Natural Language Processing (NLP), Pattern Recognition and Web Scrapping techniques for automatically extracting external cross-references from contractual requirements and prepare a map for representing related external cross-references …

A Requirements Measurement Program For Systems Engineering Projects: Metrics, Indicators, Models, And Tools For Internal Stakeholders, Ibtehal Noorwali

Electronic Thesis and Dissertation Repository

Software engineering (SE) measurement has shown to lead to improved quality and productivity in software and systems projects and, thus, has received significant attention in the literature, particularly for the design and development stages. In requirements engineering (RE), research and practice has recognized the importance of requirements measurement (RM) for tracking progress, identifying gaps in downstream deliverables related to requirements, managing requirements-related risks, reducing requirements errors and defects, and project management and decision making.

However, despite the recognized benefits of RM, research indicates that only 5\% of the literature on SE measurement addresses requirements. This small percentage is reflected in …

A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki

Electronic Thesis and Dissertation Repository

Through social media platforms, massive amounts of data are being produced. Twitter, as one such platform, enables users to post “tweets” on an unprecedented scale. Once analyzed by machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. This thesis describes a visual analytics system (i.e., a tool that combines data visualization, human-data interaction, and …

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

The Unpopularity Of The Software Tester Role Among Software Practitioners: A Case Study, Yadira Lizama, Daniel Varona, Pradeep Waychal, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

As software systems are becoming more pervasive, they are also becoming more susceptible to failures, resulting in potentially lethal combinations. Software testing is critical to preventing software failures but is, arguably, the least understood part of the software life cycle and the toughest to perform correctly. Adequate research has been carried out in both the process and technology dimensions of testing, but not in the human dimensions. This work attempts to fill in the gap by exploring the human dimension, i.e., trying to understand the motivation/de-motivation of software practitioners to take up and sustain testing careers. One hundred and forty …