With the rapid advancement of technologies, software is gaining its popularity in assisting our daily activities in the last decades. This circumstance causes a rising concerns about a software product with high quality which lead to a question about the justification whether a software product has high quality. Therefore, a numerous of researches and studies had spent a lot of effort in software product quality assessment in order to justify whether the software product(s) under study have satisfactory quality. One of the foremost approaches to assess software product quality is the application of the quality models. For example, quality model ISO 9126. However, the quality models do not provide an explicit way to aggregate the performance of different quality aspects nor handling the various interests raised from different perspective or stakeholders. Although many studies have been conducted to aggregate the different measures of quality attributes, they are still not capable to include the various interests raised by different software product stakeholders. Therefore, some studies have attempted to apply MCDA methods in order to aggregate the measure of quality attributes as the ultimate software product quality and handling the various quality interests. However, they do not provide any rational about their particular choice of MCDA methods. Most of them justify their choice by referring to high popularity of the selected MCDA method. Without studying the suitability of MCDA methods in the application domain of the software product, it is difficult to conclude whether the chosen MCDA methods fit in the intended software engineering discipline. Furthermore, there is no systematic approach available to help other software practitioners in selecting the MCDA method that will be suitable for their needs and constraints in software product quality assessment. This thesis aims to provide the key concepts for an effective selection of suitable MCDA method for the purpose of software product quality assessment. A foremost part of this thesis presents two systematic reviews. The first review illustrates the evaluation of the characteristics of MCDA methods. The second review identifies the major needs and constraints of the software quality assessment potential MCDA method has to consider in order to be used for assessing quality of software products. Based on the results from both systematic reviews, a selection framework named MCDA-SQA framework is formulated. This framework is intended to assist the software practitioners to systematically select and adapt appropriate MCDA method(s) in order to fulfil their quality assessment needs and the respective environmental concerns.

Assessing the source code quality of software objectively requires a well-defined model. Due to the distinct nature of each and every project, the definition of such a model is specific to the underlying type of paradigms used. A definer can pick metrics from standard norms to define measurements for qualitative assessment. Software projects develop over time and a wide variety of re-factorings is applied tothe code which makes the process temporal. In this thesis the temporal model was enhanced using methods known from financial markets and further evaluated using artificial neural networks with the goal of improving the prediction precision by learning from more detailed patterns. Subject to research was also if the combination of technical analysis and machine learning is viable and how to blend them. An in-depth selection of applicable instruments and algorithms and extensive experiments were run to approximate answers. It was found that enhanced patterns are of value for further processing by neural networks. Technical analysis however was not able to improve the results, although it is assumed that it can for an appropriately sizedproblem set.