http://hdl.handle.net/123456789/10362 2026-04-04T12:27:57Z 2026-04-04T12:27:57Z Farrukh Shahzad Ahmed, 01-281162-001 http://hdl.handle.net/123456789/16916 2024-01-19T06:19:28Z 2023-01-01T00:00:00Z

Applications Of Value-Centric Regression Testing For Software Applications Farrukh Shahzad Ahmed, 01-281162-001 In the current era, businesses are Information Technology (IT) reliant, but most companies are deteriorating to maximize the value of their IT initiatives to their businesses. IT professionals do not know the value of distinct software features to the business. Likewise, they do not know the business value of diverse software quality attributes to the business. Therefore, they prioritize their project tasks based on their perceptions without considering formally measured business value. Ignoring value in software processes, practices, and artifacts is a value-neutral approach. In regression testing, software testers cannot re-execute all the test cases to find out the ripple effects of the changes due to time and budget constraints. No company can afford exhaustive regression testing in rapidly growing applications. Therefore, software testing professionals need a way through which they can prioritize their test cases for regression testing to uncover maximum bugs and side effects by utilizing minimum time and cost. Test Case Prioritization (TCP) is one of the processes to address this challenge. TCP is a smart way for regression testing to handle testing resource constraints. The main advantage of TCP is to save time through the prioritization of critical tests earlier. Current TCP techniques can be categorized as Value-Neutral (VN) and Value-Based (VB) approaches. In a VB approach, the cost of test cases and severity of faults are considered while, in a VN approach these are not considered. The VN approach is dominant over VB approach, and it assumes that all test cases have identical costs and that all software faults have same severity. But this notion seldom holds in practice. Therefore, VN TCP techniques are likely to deliver unreliable results. To fill this gap, focus should be shifted from VN to VB test prioritization. Presently, limited research work is done in a VB approach. To address this issue, a Systematic Literature Review (SLR) of VB TCP techniques is performed, and its results are presented in this thesis. Its purpose is to combine the overall knowledge related to VB TCP techniques and to highlight some open research issues in this domain. The literature review yields that value-orientation is vital in the TCP process to achieve its targeted goals and this is a potential area for further research. Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have identical costs, and all faults have equal severity. Using APFD for performance evaluation of test case orders where test case cost or fault severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results. To the best of the author’s knowledge, there is no formal technique available to quantify business value based on which test cases can be prioritized. To overcome this problem, a business value quantification model has been proposed in this work to estimate fault severities and test case cost. The proposed model supports the business value measurement of software requirements. We use the term software features as functional requirements and software quality attributes as nonfunctional requirements. The business value calculation of software features and quality attributes is based on three factors client priority, feature complexity, and feature usage. To compute the value of client priority, the proposed model utilizes five business success factors including profitability, productivity, operational efficiency, customer satisfaction, and time to market. Software fault severity and test case cost are estimated through the business value of requirements because different test cases and faults are directly associated with some requirements. Business value has been incorporated into the TCP process through the proposed model. The model is validated through two working examples. Based on the proposed model, two value-based TCP techniques have been introduced in this thesis using Genetic Algorithms (GA). These techniques are Value-Cognizant Fault Detection-Based TCP (VCFDB-TCP) and Value-Cognizant Requirements Coverage-Based TCP (VCRCB-TCP). Two novel value-based performance evaluation metrics are also introduced for value-based TCP including the APFDv and Average Percentage of Requirements Coverage per value (APRCv). Two case studies are performed to validate proposed techniques and performance evaluation metrics quantitatively. A statistical analysis of the results is performed by a statistical test. The statistical results reveal that the proposed approaches provide significantly better results than traditional value-neutral TCP techniques. Supervised by Dr, Tamim Ahmed Khan

2023-01-01T00:00:00Z Zulfiqar Ali, 01-281151-001 http://hdl.handle.net/123456789/16918 2024-01-19T06:46:53Z 2023-01-01T00:00:00Z

Services Provisioning By Using Intelligent Learning For Long Range Wide Area Network (LoRaWAN) Zulfiqar Ali, 01-281151-001 The exponential growth of Internet of Things (IoT) services and ecosystems recently emerged with a novel type of communication network known as Low Power Wide Area Network (LPWAN). This standard enables low-power long-range communication at a low data rate. Besides, Long Range Wide Area Network (LoRaWAN), is a recent standard of LPWAN that incorporates LoRa Wireless into a networked infrastructure. Consequently, Quality of Service (QoS) efficient service provisioning is a major challenge due to the highly dense network environment, the limited battery lifetime of LoRa-based End Devices (EDs), spectrum coverage, and data collisions. Intelligent and efficient service provisioning is a dire need of a network to streamline and address these problems. This study proposes a novel and Intelligent Learning (IL) based framework for efficient service provisioning without placing any extra burden on the network and its resource constraint LoRaWAN EDs. The proposed framework intelligently learns from varied underlying potential parameters such as real-time Packet Error Rate, data throughput, data delay, data collisions, and energy consumption to improve the overall network performance. The proposed framework is extensively simulated and evaluated with current state-of-the-art benchmark algorithms using standard and extended evaluation metrics. Slotted Aloha with Markov chain model mitigates collision and enhances the performance of LoRaWAN by 38% in terms of data throughput. Results of Slotted Aloha with Markov chain model are compared with Pure Aloha used by conventional LoRaWAN. Adaptive Scheduling Algorithm (ASA) with Gaussian Mixture Model (GMM) is extensively compared with conventional LoRaWAN and Dynamic PST (Priority Scheduling Technique). ASA with GMM enhanced performance in terms of delay by 5% in the LoRaWAN environment. Dynamic Reinforcement Learning Resource Allocation significantly reduced the energy consumption of EDs by 20% measured in Joules. Results of Dynamic Reinforcement Learning Resource Allocation is compared with conventional LoRaWAN and Adaptive Priority-aware Resource Allocation (APRA). The proposed work is properly cross-validated to utterly show unbiased results. Supervised by Dr, Kashif Naseer Qureshi

2023-01-01T00:00:00Z Khurrum Mustafa Abbasi, 01-281152-002 http://hdl.handle.net/123456789/16917 2024-01-19T06:33:51Z 2023-01-01T00:00:00Z

A Unified Modeling Framework For Internet Of Things (Iot) Systems Khurrum Mustafa Abbasi, 01-281152-002 The Internet of Things (IoT) is an emerging field, and the growth of IoT devices is a rapidly increasing trend. These devices are expected to combine both heterogeneity and smartness, enabling them to provide services for both humans and other devices. Due to this, it is crucial to have an appropriate modeling framework in place for modeling the integration of these devices. Modeling IoT systems require the consideration of several aspects, including the selection of discrete or continuous mathematical models, computational simulation, or a combination of these. The selection of modeling approaches or frameworks is also crucial in this process. This research provides a unified modeling framework by integrating multiple frameworks and approaches. Our research began with exploring, deducing, and inducting the research problem. We then built a hypothesis that a unified framework can be developed for modeling complex IoT systems. To achieve this, we first formulated sub-frameworks based on the architectural components of IoT systems. These sub-frameworks were then integrated into a unified framework, providing a way to model the behavior of service-oriented internet-based devices and systems in complex scenarios. The unified framework is based on three distinct sub-frameworks for modeling IoT systems. The first sub-framework is aimed at modeling IoT systems from the Software Engineering viewpoint. The second sub-framework is focused on modeling IoT systems that have fuzzy values, such as values that fall between 0 and 1, giving rise to fuzzy logic. The third sub-framework is aimed at modeling IoT systems that have ambient entities in their composition. An ambient entity is an entity that must possess the properties of mobility, inclusion, and narrowness, such as a bus or an airplane. We used our framework to model case studies and compared our framework with the exiting. This study is expected to make a significant contribution to the modeling paradigm employed in understanding, analyzing, and designing IoT systems. With the proposed unified framework, it is possible to model the behavior of IoT systems in a comprehensive manner, considering the different perspectives and aspects that are involved in the modeling process. This will enable the development of more robust and reliable IoT systems, which can better serve the needs of both humans and other devices. Supervised by Dr, Tamim Ahmed Khan

2023-01-01T00:00:00Z