M.Phil / MS

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A Deep Learning Model for Efficient Effort Estimation in Scrum Projects
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Ahmad Amjad; FA18-RCS-028; LHR TP 8041; Dr. Farrukh Zeshan
Software Development Effort Estimation (SDEE) is defined as the process of predicting the effort required to develop a software system. Effort estimation at the earlier stage in scrum project is specially a challenging task because the requirements are volatile in nature and it can be changeable at any phase of the whole project life cycle. A project manager needs estimation for making a decision and predicting the total budget. The success or failure of software development depends on the accurate estimation of efforts required to complete a project. In literature, the three basic ways are explored for efforts estimation in scrum projects which are purely model based method, the expert-based methods and hybrid-based methods. In the model-based method the data collected from similar previous project to make prediction about the new project, the expert-based method totally dependent on human expertise to make such kind of judgement whereas the hybrid method are the combination of both model based and expert based methods. Every method has different challenges but in the era of agile software development methodologies, traditional planning and software effort estimation methods are replaced to meet customer’s satisfaction in agile environments. However, software effort estimation remains a challenge. Although teams have achieved better accuracy in estimating story points effort required to implement user stories or issues, these estimations mostly rely on subjective assessments, leading to inaccuracy and impacting software project delivery. Some researchers are pointing good results by the adoption of deep learning to address this issue. Given the foregoing, this study proposes a deep learning model for story points estimationin t h e scrum projects. In this study proposed a model which used the combination of Bidirectional LSTM and Recurrent Highway Network two powerful deep learning techniques using the story points for efforts estimation in the scrum projects. The proposed model is trained over a large d a t a s e t s for story points estimation made by 16 open-source projects which have 23313 issue that are large story point datasets according to my knowledge. The performance of the proposed model has validated through the evaluation techniques such as Mean absolute error (MAE), Mean square error (MSE), and Median absolute error (MdAE). The results shown that the proposed model provides betters result as compared to previous approaches
Requirement Change Prediction Model For Small Software systems
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Rida Fatima; FA18-RCS-025; LHR TP 6416; Dr. Farrukh Zeshan
The software industry is rising gradually, it seems that there is no end to software development. Modern era software projects are complex and consist of many components, so change is unavoidable in these projects. Therefore, change in the software requirements document must be predicted at an early stage to preserve the resources. There are a lot of requirement change prediction models proposed in the literature and they are addressing a smaller number of variables and including experts knowledge for analyzing the requirement specification document. In this work, we have focused on the small software systems which have focused few variables of requirement change prediction model.The variables have a major effect on the software requirements and these are analyzed by the stakeholders, developers and experts with the questionnaire method. Their knowledge is incorporated in the Bayesian network as conditional probabilities of independent and dependent variables.This is actually the proposed model for software requirements change prediction.We used an algorithm with the model by utilizing variable elimination method to obtain the posterior probability of the revisions in software requirement document. However this model can also be used for the large software systems because it is effecent to resolve their problems also. the The proposed model is evaluated by hypothesis testing, sensitivity analysis and by comparing with the existing models. The results obtained proved to be promising as it decreases the probability of revisions in the requirement document. We obtained the 0.42 probability for the revisions in the requirement document so the results have improved the existing model predictions

M.Phil / MS

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