Department of Computer Science

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Author: search.filters.author.Dr. Farooq AhmadSubject: search.filters.subject.Information technology

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    Towards Automatically Resolving Biasness and Conflicts in Stakeholders Input for Requirement Prioritization
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Nabiha Yaseen; SP19-RCS-026; Dr. Farooq Ahmad; LHR TP 8043
    Requirements are needed before developing any project, so these requirements are elicited from the stakeholders. Software Engineers elicit these requirements to meet the demands of the stakeholders. Software engineers find it challenging to satisfy the demands of multi stakeholders if these elicited requirements are vague or misinterpreted. To meet the accuracy, satisfaction, and expectations of multi stakeholders, prioritization of the elicited requirements is needed. Prioritizing the elicited requirements can completely prevent contract breaches or violations like budget overruns, project deadline mismatches, overshooting of delivery dates, and omitting crucial requirements during development. There are various techniques have been introduced in this area, but the problems related to RE are still under research in terms of lack of collaboration among multi stakeholders, Developer’s effort in eliciting conflicted requirements and to make changes after, and resource and time waste. To address these problems, we designed an approach called the Collaborative Requirements Elicitation Tool (CRET), which is intended to facilitate and improve efficient collaboration between multiple users to get the best from requirements elicitation. To enable multiple stakeholders to cooperatively elicit the same goals and requirements toward project that are conflict-free and unbiased, our tool has been designed and developed in a way to support the real-time communication and collaboration among them and to get the requirements on which they are collaboratively satisfied. Our proposed tool was evaluated for its ability to get best accuracy measures to eliciting the requirements in an unbiased, conflict-free, collaborative, timely efficient and lowering the developer’s effort manner. In our proposed tool CRET, requirement elicitation was done collaboratively, by using the Point P rating technique to set the requirement prioritizing values and employed the k-mean clustering algorithm prioritize the elicited requirements. Introduced two new features in this research is the report requirement feature and other is authority approval to remove the conflicts before forwarding the requirements for prioritization. We utilized the PHP LARAVEL framework to develop this web-based tool. With groups 2 of requirement engineers and client stakeholders, we conducted a user study to evaluate the tool, focusing on requirement elicitation and prioritization using CRET. The study's findings indicate that our proposed automated tool support can assist multiple stakeholders in collaborative communicating with other stakeholders more effectively to produce better and improved requirement elicitation, and our tool eliminates conflicts and biases of multiple stakeholders during elicitation through their collaboration. Our tool works in the four operational steps collaboratively RE (requirement elicitation), RRR (Req rating and reviewing), AA (authority approval) and RP (requirement prioritizing). In the RE phase of the software development life cycle; RP is a task that relates to multi phase decision making. It is mostly used for software release planning and influences the creation of the best software product by eliciting stakeholders' preferred needs.
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    An Improved Technique for Software Requirement Prioritization using Functional Requirements
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Zunaira Jamil; FA19-RCS-016; Dr. Farooq Ahmad; LHR TP 8341
    Earlier recognition, categorization, and prioritization of elicited requirements in the software development is essential for the project's success because it allows problems to be identified and classified in early stages. The most critical and difficult stage of the Software Development Life Cycle is gathering requirements Following this stage, the requirement priority should be handled and saved for upcoming projects. Using machine learning classification approaches software requirement prioritization can be predicted during the requirement gathering stage. To predict the priority of functional requirements for developing new software, a dataset having the attributes of software requirements is required. In this research, a dataset named software requirement prioritization(SRP) is presented that includes requirements from Software Requirement Specification (SRS) of several open-source projects' and ranking value of different factors that help in priority prediction. Ranking value is obtained from the IT experts through survey on rating scale of 1 to 5 where 1 ranks lowest and 5 ranks highest. Further for our proposed SRP system, content-based n-gram approach is considered at word and character level. After that combine multi-label classifier i.e. One Vs Rest, Label Powerset, with six classical machine learning classifiers i.e. Linear SVC, Logistic Regression etc. Micro F1 score, Accuracy score, and Hamming loss are used for evaluation. However, when compared to the other combination of OVR and Label power set with Gradient Boost Classifier, Random Forest, Nave Baise, linear SVC, Bagging, and Adaboost, Label Powerset with Linear SVC performed well.
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    Petri Net Based Formal Semantic Extraction from Code
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Shafaq Naz; SP19-RCS-017; LHR TP 7293; Dr. Farooq Ahmad
    Due to the rise of reverse engineering practices from the last two decades, worth of design engineers become highlighted. Therefore, the point of concern is the verifiability of the reverse engineered architecture, because these practices are more likely to use in safety critical systems. Hence, a prototype is needed to verify or to validate the reverse engineered design or module of the legacy system to use in further applications to evade future loss. Therefore, dynamic analysis of the system is main concern which is not possible through direct reversing from the source code. It may include multiple execution trace analysis of the system. Generating a diagram, representing the design of the application, from the code helps to study the runtime behavior of the application. However, diagrams are an informal representation of the behavior which do not support verification and validation of the application. Therefore, there is a need to generate the formal representation of the application. This research focus on the automatic generation of Petri net models from code which describe the behavior of the application as well as its verification. Moreover, generating Petri net formal model from code is a challenging task which is the focus of this research. Firstly, a systematic literature review and comparison of different reverse engineering tools will be performed to critically find out the research gap. Further, this research will proceed to provide a framework to reverse engineer the formal representation of code to understand the behavior the system
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    Computer Vision Based Pattern Matching and Displacement Detection During Fabric Printing
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Muhammad Abdul Qayum; FA10-PCS-001; LHR TP 5799; Dr. Farooq Ahmad
    There is an absolute need for the robust quality inspection system, which can take on the new challenges and overcome the human limitations to inspect the fabric production consistently. To address this gap, computer vision based techniques are employed by enterprises. Some of the beneficent manufacturers include ceramic, circuit board printer, paper printer and textiles. Excellent work has been done in the yarn and weaving production sections of textile industry, however fabric printing monitoring is being explored by the researchers. The referential approach is mostly adopted to monitor fabric printing for defect detection. It works by acquiring an error-free image and registering this image with subsequent images captured during the production. In this method, the major time-consuming problem is to find the design repeat in the sample image before further processing. The researchers suggested some methods like distance matching in RGB space, photo encoders, genetic algorithm along with recursive splitting, Fuzzy C-means clustering, and cross correlation algorithm. There are certain limitations with these methods, for instance, sensitive to small deformation, required extra hardware prone to mechanical fault and it is expensive in calculations. In this research, a method for finding the design repeat is proposed. The method is tuned up according to the textile printing domain. Since the fabric moves in one direction during printing process, therefore the image registration can be confined to the same direction. To further speed up the process, the image registration method is applied using few initial pixel columns of the reference image with the sample image. Such bunch of selected columns of the reference image is matched with the same number of columns selected from sample image by moving this bunch on the sample image column by column. The maximum matching position is marked as the start of the design repeat. As the repeat size is always fixed, therefore complete design can be extracted from acquired image for defect detection. The experimental results on different fabric designs using the above-mentioned method are promising. Moreover, xi this technique improves the image alignment speed which enhances defect detection system performance. The most common defect is the displacement or misregistration of a colour. This defect is caused by the misalignment of cylindrical screens of fabric printing machine. As every screen is responsible for a colour, the misaligned screen registers the colour on the incorrect position. It leads to defective production and contributes to a substantial loss of the material and time. Few researchers proposed the feature based solution which are either computationally expensive or do not provide detail information about the defect like location and colour. Further, some techniques depend on the additional algorithms to find defects. Mostly, these methods depend on the accuracy of reference and sample image alignment. So there is always a chance of wrong notification. An improved algorithm for the detection of displacement of a colour during fabric printing is proposed in this thesis. The algorithm concatenates red, green and blue pixel values of the RGB image to represent a colour and later produces a frequency distribution of different colours for both sample and reference images. The colours with low frequencies are removed considering noise. In the next step, colours are grouped depending upon their distances from each other. These colour groups are later used to detect any new colour in the sample image which is actually created by the displacement defect and colour variation. The proposed algorithm successfully detects displacement and colour variation defects when it is tested using different flawed printed fabric images. The result shows that the proposed method has almost same accuracy as stat of the art algorithm and more helpful to detect the colour displacement, and it can also overcome the shortcoming of repeat finding process.