Thesis Title: A Wimax Uplink Scheduling Algorithm among Fixed Stations
Author: Lubna Fathi Mehdawi, Supervisor: Prof. Mohamed Bettaz, Year: 2017
Faculty: Information Technology, Department: Computer Science

Abstract: IEEE 802.16 defines WiMAX as a broadband access technology that, among others might be considered as an alternative to other broadband access technologies such as fiber optics and 4G. It is worth to mention that IEEE802.16 does not impose any scheduling technique, thus opening the way for contributions from industry and academia aiming at enhancing the Quality of Service (QoS). This thesis proposed an uplink scheduling algorithm, the aim of this work is twofold: At the theoretical level, firstly we project to deepen the study of a promising scheduling algorithm based on the Deficit Round Robin (DRR), namely the Dynamically Weighted Deficit Round Robin (DWDRR) (Andreadis et al., 2014), secondly studying different parameters especially the Queue delay to enhance the performance for different flows rate and improving the QoS. This will improve the scheduling technique to provide user with better performance with respect of different Quality of service classes. At the experimental level, we project to adapt the NS-2 simulator by functionalities able to deal with the 802.16 standard, then to use it to perform simulations, with the objective to compare our scheduling algorithm with the DWDRR, with respect to throughput, delay and fairness for various QoS connections.

Keywords: Wimax, Uplink, Scheduling, Algorithm

Thesis Title: Using NLP for Semi Automated Class Diagram Generation from Arabic Text
Author: Linah Ghazi Al-Khatib, Supervisor: Dr. Samir Tartir, Year: 2017
Faculty: Information Technology, Department: Computer Science

Abstract: Arabic language is one of the most used languages in the world. Software requirements of an information system are written in natural language; Arabic or English. Arabic is the formal language that is used to write software requirements by the most of ministries and governmental institutions in Arab countries. In software engineering, manual analysis and design of the requirements cause variations of the obtained model due to the complexity of natural language. Using natural language processing tools in analysing and transforming requirements into models enhances knowledge extraction performance. Many approaches addressed this domain, but very little research has been developed to obtain models from requirements written in Arabic, especially class model. Also, they never use semantic approaches that utilize the domain ontologies, which produce a gap between knowledge engineering and software engineering. In this thesis, we propose an approach that solve the previous problems and automate the process of constructing a class model from Arabic requirements. The core aim of our approach is to handle Arabic user requirements transformation into class diagrams. We propose a set of identification rules to identify class diagram elements, then we combine the proposed set of identification rules, ANLP tools and domain ontologies to build a unique approach for generating UML class diagram from Arabic user requirements. Our approach is the first approach that utilizes domain ontology and Arabic natural language processing tools to generate class diagrams form the Arabic requirements. Finally, we validate our approach by performing an experiment, which involves a group of human evaluators. The results showed that our approach produces very good results as compared to the evaluators depending on the performance evaluation criteria; precision and recall. The results indicated that our proposed technique is a good candidate for extracting class diagrams from Arabic user requirements text as compared to existing approaches.

Keywords: NLP, Class Diagram, Arabic Text, Automated

Thesis Title: A Software Feature-based Reverse Engineering Methodology
Author: Anas Adnan Alhamwieh, Supervisor: Prof. Said Ghoul, Year: 2017
Faculty: Information Technology, Department: Computer Science

Abstract: Software reverse engineering is the kernel task of software maintenance. In the past years it deals with software source code model understanding. This model is at an implementation level, detailed, language depending, and complex. Nowadays, the software reverse engineering is levered to software abstract design level, supported by feature model notations, language independent, and more simple that code reading. The recent approaches to feature based software reverse engineering face following insufficiencies: lack of a complete integrated methodology, adapted feature model, feature patterns recognition, and Graph based slicing. The works presented in this thesis propose some solutions to the above challenges through an integrated methodology. In fact, the proposed methodology starts by presenting elementary and configuration features in a uniform way by introducing specific attributes. The reverse engineering process is the supporting feature pattern recognition which allow the understandability of any feature (is it elementary?, is it a configuration and what features is it composed by?, what relations it has?, etc.). It also support software feature model slicing, not based on mathematical notations but on graphs ones which is more adapted to software reading. The slicing criteria are rich enough to allow answering questions of software maintainers. They covers the two main relations in the software feature model (AND, OR) and the two main directions (forward and backward). A comparison of this proposed methodology, based on effective criteria, with the similar works seems to be valuable and competitive (the enrichment of the feature model and feature pattern recognition were never approached and the proposed slicing technique is more general and applicable).

Keywords: Feature-based, Reverse Engineering

Thesis Title: Self-Adaptive Software to Unpredicted Relevant Events
Author: Shatha Mohammed Alfar, Supervisor: Prof. Said Ghoul, Year: 2017
Faculty: Information Technology, Department: Computer Science

Abstract: Self-Adaptation software has been used in software development and in lots of organizations to correspond with changing requirements and environments which has been used successfully to deal with planned and predicted problems. Lots of self-adaptation works rely on bio-inspired approaches dealing with external behaviours but they never dealt with the internal ones. The idea of modeling both external and internal behaviours along with the integration of predicted and unpredicted events handling is a real actual challenge. Inspired by the natural genetics, this thesis proposes a solution to the above challenge. It consists of a self-adaptive software framework integrating both external and internal software behaviours along with predicted and unpredicted events handling. It models the changeability of the software during the evolutionary lifecycle from a state to another state against planned events (scheduled occurrence: like evolution event) as well as unplanned event (occurring randomly: like faults). The obtained result, compared to the actual approaches, are valuable.

Keywords: Self-Adaptive, unpredicted

Thesis Title: A Bio-inspired UML Use Case Variability Modelling
Author: Esraa Yousef Abdel-Ghani, Supervisor: Prof. Said Ghoul This, Year: 2017
Faculty: Information Technology, Department: Computer Science

Abstract: Recently, Software Product Lines (SPLs) is a widely used approach to represent the variability modelling in variant software products. Feature Model (FM) is the most important technique used to define and manage the variability through products in SPLs. Software requirements variability model, simplifying the maintenance process, is an emerging approach in software engineering and is therefore important. One of ways to represent the software requirements is by using UML use case model. The requirements' modelling using UML use case is still a challenge because it needs specific model for each system requirements, and it does not support variability meta modelling at several levels. Many researches dealt with use case variability modelling, but all of them are not inspired from nature, consequently far from real world, and the feature models they are based on are not covering all use case concepts. In addition, the current approaches are not supported by a complete modelling methodology guiding from domain variability modelling in use cases to application specific use cases generation. So, this thesis proposes a complete and formal methodology dealing with the above shortages. Its process is carried out through variable domain use case meta modelling, variable application use case modelling, and specific application use case generating. Bio-inspired, it decreases the gap between computing concepts and real world ones. It supports use case variability modelling by introducing versions and revisions features and related relations. A meaningful evaluation of the proposed approach necessitates a real industrial software production scale and over a broad time period, which is out of scope of this thesis. However, a comparative evaluation with the closest recent works, upon some meaningful criteria in the domain, shows the conceptual value of the proposed methodology and leads to promising research perspectives.

Keywords: UML, Use Case, Variability, Modelling