Predicting Software Reliability Using Ant Colony Optimization Technique with Travelling Salesman Problem for Software Process – A Literature Survey

Computer software has become an essential and important foundation in several versatile domains including medicine, engineering, etc. Consequently, with such widespread application of software, there is a need of ensuring software reliability and quality. In order to measure such software reliability and quality, one must wait until the software is implemented, tested and put for usage for a certain time period. Several software metrics have been proposed in the literature to avoid this lengthy and costly process, and they proved to be a good means of estimating software reliability. For this purpose, software reliability prediction models are built. Software reliability is one of the important software quality features. Software reliability is defined as the probability with which the software will operate without any failure for a specific period of time in a specified environment. Software reliability, when estimated in early phases of software development life cycle, saves lot of money and time as it prevents spending huge amount of money on fixing of defects in the software after it has been deployed to the client. Software reliability prediction is very challenging in starting phases of life cycle model. Software reliability estimation has thus become an important research area as every organization aims to produce reliable software, with good quality and error or defect free software. There are many software reliability growth models that are used to assess or predict the reliability of the software. These models help in developing robust and fault tolerant systems. In the past few years many software reliability models have been proposed for assessing reliability of software but developing accurate reliability prediction models is difficult due to the recurrent or frequent changes in data in the domain of software engineering. As a result, the software reliability prediction models built on one dataset show a significant decrease in their accuracy when they are used with new data. The main aim of this paper is to introduce a new approach that optimizes the accuracy of software reliability predictive models when used with raw data. Ant Colony Optimization Technique (ACOT) is proposed to predict software reliability based on data collected from literature. An ant colony system by combining with Travelling Sales Problem (TSP) algorithm has been used, which has been changed by implementing different algorithms and extra functionality, in an attempt to achieve better software reliability results with new data for software process. The intellectual behavior of the ant colony framework by means of a colony of cooperating artificial ants are resulting in very promising results. Keywords: Software Reliability, Reliability predictive Models, Bio-inspired Computing, Ant Colony Optimization technique, Ant Colon

Identification, Analysis & Empirical Validation (IAV) of Object Oriented Design (OO) Metrics as Quality Indicators

Metrics and Measure are closely inter-related to each other. Measure is defined as way of defining amount, dimension, capacity or size of some attribute of a product in quantitative manner while Metric is unit used for measuring attribute. Software quality is one of the major concerns that need to be addressed and measured. Object oriented (OO) systems require effective metrics to assess quality of software. The paper is designed to identify attributes and measures that can help in determining and affecting quality attributes. The paper conducts empirical study by taking public dataset KC1 from NASA project database. It is validated by applying statistical techniques like correlation analysis and regression analysis. After analysis of data, it is found that metrics SLOC, RFC, WMC and CBO are significant and treated as quality indicators while metrics DIT and NOC are not significant. The results produced from them throws significant impact on improving software quality

A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

Software quality is an important research area and has gain considerable attention from software engineering community in identification of priority quality attributes in software development process. This thesis describes original research in the field of software quality model by presenting a Feature Ranking Algorithm (FRA) for Pragmatic Quality Factor (PQF) model. The proposed algorithm is able to improve the weaknesses in PQF model in updating and learning the important attributes for software quality assessment. The existing assessment techniques lack of the capability to rank the quality attributes and data learning which can enhance the quality assessment process. The aim of the study is to identify and propose the application of Artificial Intelligence (AI) technique for improving quality assessment technique in PQF model. Therefore, FRA using FRT was constructed and the performance of the FRA was evaluated. The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. The result shows that the performance of FRA correlates strongly to PQF model with 98% correlation compared to the Kolmogorov-Smirnov Correlation Based Filter (KSCBF) algorithm with 83% correlation. Statistical significance test was also performed with score of 0.052 compared to the KSCBF algorithm with score of 0.048. The result shows that the FRA was more significant than KSCBF algorithm. The main contribution of this research is on the implementation of FRT with proposed Most Priority of Features (MPF) calculation in FRA for attributes assessment. Overall, the findings and contributions can be regarded as a novel effort in software quality for attributes selection

A Review of Metrics and Modeling Techniques in Software Fault Prediction Model Development

This paper surveys different software fault predictions progressed through different data analytic techniques reported in the software engineering literature. This study split in three broad areas; (a) The description of software metrics suites reported and validated in the literature. (b) A brief outline of previous research published in the development of software fault prediction model based on various analytic techniques. This utilizes the taxonomy of analytic techniques while summarizing published research. (c) A review of the advantages of using the combination of metrics. Though, this area is comparatively new and needs more research efforts

Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to contend with the stresses of the business environment, the policies, and procedures dictated by their organizations, and resource constraints. The second purpose of this research study is to propose methods by which conventional project assessment processes can be enhanced using quantitative methods that utilize real-time project execution data. Transferability of academic research to industry application is specifically addressed vis-à-vis a delivery framework to provide meaningful data to industry practitioners

Semi-supervised and Active Learning Models for Software Fault Prediction

As software continues to insinuate itself into nearly every aspect of our life, the quality of software has been an extremely important issue. Software Quality Assurance (SQA) is a process that ensures the development of high-quality software. It concerns the important problem of maintaining, monitoring, and developing quality software. Accurate detection of fault prone components in software projects is one of the most commonly practiced techniques that offer the path to high quality products without excessive assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. However, collection of fault data at module level, particularly in new projects, is expensive and time-consuming. Semi-supervised learning and active learning offer solutions to this problem for learning from limited labeled data by utilizing inexpensive unlabeled data.;In this dissertation, we investigate semi-supervised learning and active learning approaches in the software fault prediction problem. The role of base learner in semi-supervised learning is discussed using several state-of-the-art supervised learners. Our results showed that semi-supervised learning with appropriate base learner leads to better performance in fault proneness prediction compared to supervised learning. In addition, incorporating pre-processing technique prior to semi-supervised learning provides a promising direction to further improving the prediction performance. Active learning, sharing the similar idea as semi-supervised learning in utilizing unlabeled data, requires human efforts for labeling fault proneness in its learning process. Empirical results showed that active learning supplemented by dimensionality reduction technique performs better than the supervised learning on release-based data sets

Makine öğrenme algoritmaları kullanılarak yazılım hata kestiriminin iyileştirilmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.YÖK tez kataloğunda erişimi mevcut değildir