A study on modelling performance in readiness review process and deep learning for automatic project effort estimation

Name: Tachanun Kangwantrakool

keyword: Modelling performance

; Readiness review process

; Deep learning

Abstract: This research comprises of two separate papers about the enhancement of software process management. In the first work, the author proposes a so-called Reengineering Readiness Review Process Performance (R3P2) model, in which a set of performance metrics and an integrated toolkit, namely the PPM Suite, are equipped to solve the problem of the complex and laborious nature of an evaluation of an organization's CMMI practice. The model contains (1) a document management tool for reusable shared papers, (2) a project management mechanism to assist mapping between documents and CMMI principles, and (3) a set of metrics to standardize process improvement by offering a common understanding of how to define quality. The R3P2 model is evaluated using actual assessments of 308 case studies collected from 28 nations. The model performance interpretation enabled a holistic analysis of the score collected during the evaluation process for four primary metrics: maturity level delivery, customer satisfaction, cycle time delay, and assessment gap ; and four secondary metrics: customer expectation, cycle time delay variability, readiness review process capacity, and gap variability. The outcome revealed that the performance of R3P2 was promising in the majority of criteria. Using the data set produced from the first work, the author presents a deep learning approach to estimate the project effort, which reflects the cost, in the second work. This dataset includes the 1,573 pairs of a software project description written in English and its actual man-month, gathered from the 308 companies for CMMI appraisal. Unfortunately, the dataset is not publicly available due to the privacy issue of undisclosable clients’ confidential information. For benchmarking of the model performance, the author purchased the additional datasets of 9,100 software projects from ISBSG1. The data was collected from several leading IT companies around the world. Therefore, the author's approach was evaluated using these two datasets. The total number of projects is 10,673 software projects. The data of each project is a pair of a raw project description and a structured data table explaining the character of a software project. The long-short-term memory (LSTM) sequence model is applied as the engine to estimate the project effort. In comparing four architectures ; (1) average word-vector with Multi-layer Perceptron (MLP), (2) average word-vector with Support Vector Regression (SVR), (3) Gated Recurrent Unit (GRU) sequence model, and (4) Long short-term memory (LSTM) sequence model in terms of man-months difference, the LSTM sequence model achieves the lowest and the second-lowest mean absolute errors, which are 0.705 and 14.077 man-months for 9-LAs’ datasets and ISBSG datasets, respectively. The MLP model achieves the lowest mean absolute errors, which are 14.069 for ISBSG datasets

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Thanaruk Theeramunkong

Role: advisor

Created: 2023

Modified: 2024-07-29

Issued: 2024-07-29

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2023.104

eng

DegreeName: Doctor of Philosophy

Level: Doctoral Degree

Descipline: Engineering and Technology

Grantor: Thammasat University

©copyrights Thammasat University

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