Software Defect Prediction using Deep Learning by Correlation Clustering of Testing Metrics

The software industry has made significant efforts in recent years to enhance software quality in businesses. The use of proactively defect prediction in the software will assist programmers and white box testing in detecting issues early, saving time and money. Conventional software defect prediction methods focus on traditional source code metrics such as code complexities, lines of code, and so on. These capabilities, unfortunately, are unable to retrieve the semantics of source code. In this paper, we have presented a novel Correlation Clustering fine-tuned CNN (CCFT-CNN) model based on testing Metrics. CCFT-CNN can predict the regions of source code that contain faults, errors, and bugs. Abstract Syntax Tree (AST) tokens are extracted as testing Metrics vectors from the source code. The correlation among AST testing Metrics is performed and clustered as a more relevant feature vector and fed into Convolutional Neural Network (CNN). Then, to enhance the accuracy of defect prediction, fine-tuning of the CNN model is performed by applying hyperparameters. The result analysis is performed on the PROMISE dataset that contains samples of open-source Java applications such as Camel Dataset, Jedit dataset, Poi dataset, Synapse dataset, Xerces dataset, and Xalan dataset. The result findings show that the CCFT- CNN model increases the average F-measure by 2% when compared to the baseline model

Towards Developing Agent-Based KMS In Managing Knowledge of Green SD for Community of Practice

Green Software Development (GSD) is about adopting green practices in Software Development Life Cycle towards developing environmentally-friendly software products. GSD is knowledge-intensive project, which is heavily depending on sharing of green practices in Community of Practice (CoP) to develop greener software products. As knowledge sharing is an important activity in Knowledge Management (KM), this is how the power of KM comes in to support GSD. However, there is a lack of KM application in managing green knowledge of GSD. To address the research gap, this paper suggests an implementation of agent technology together with KM application in helping CoP to share knowledge of GSD. Based on Literature Review, a conceptual architecture of agent-based KM System (KMS) is proposed, with the aim of studying on how multi-agent system and KMS are working together efficiently to enhance knowledge sharing in GSD environment

An investigation of green software engineering

The urgency of sustainability concerns has intensified in recent years, sounding alarm bells over the planet's condition and prompting nearly every industry and practice to reassess their contributions to the climate crisis. Software engineering is not immune to this scrutiny. Software engineering practices significantly affect the environment and may not align with sustainability goals. Although sustainability is a relatively recent focus in software engineering, it has garnered increased attention, with numerous studies addressing various concerns and practices. Green software engineering aspires to develop dependable, enduring, and sustainable software that fulfills user requirements while minimizing environmental impacts. As this green paradigm gains traction in software engineering, practitioners must incorporate sustainability considerations into future software designs. However, despite the surge in green software engineering research, a universally accepted definition and framework remain elusive. This paper outlines green software engineering by explaining its principles, challenges, and methods for measuring and evaluating software effectiveness in this context

A survey on energy efficiency in information systems

Concerns about energy and sustainability are growing everyday involving a wide range of fields. Even Information Systems (ISs) are being influenced by the issue of reducing pollution and energy consumption and new fields are rising dealing with this topic. One of these fields is Green Information Technology (IT), which deals with energy efficiency with a focus on IT. Researchers have faced this problem according to several points of view. The purpose of this paper is to understand the trends and the future development of Green IT by analyzing the state-of-the-art and classifying existing approaches to understand which are the components that have an impact on energy efficiency in ISs and how this impact can be reduced. At first, we explore some guidelines that can help to understand the efficiency level of an organization and of an IS. Then, we discuss measurement and estimation of energy efficiency and identify which are the components that mainly contribute to energy waste and how it is possible to improve energy efficiency, both at the hardware and at the software level

Scalability performance measurement and testing of cloud-based software services

Cloud-based software services have become more popular and dependable and are ideal for businesses with growing or changing workload demands. These services are increasing rapidly due to the reduced hosting costs and the increased availability and efficiency of computing resources. The delivery of cloud-based software services is based on the underlying cloud infrastructure supported by cloud providers, which delivers the potential for scalability that follows the pay-as-you-go model. Performance and scalability testing and measurements of those services are necessary for future optimisations and growth of cloud computing to support the Service Level Agreement (SLA) compliant quality of cloud services, especially in the context of rapidly expanding quantity of service delivery. This thesis addresses an important issue, understanding the scalability of cloud-based software services from a technical perspective, which is very important as more software solutions are migrated to the cloud. A novel testing and quantifying approach for the scalability performance of cloud-based software services is described. Two technical scalability metrics for software services that have been deployed and distributed in cloud environments, have been formulated: volume and quality scalability metrics based on the number of software instances and the average response time. The experimental analysis comprises three stages. The first stage involves demonstrating the approach and the metrics using real-world could-based software service running on Amazon EC2 cloud using three demand scenarios. The second stage aims to extend the practicality of the metrics with experiments on two public cloud environments (Amazon EC2 and Microsoft Azure) with two cloud-based software serices to demonstrate the use of these metrics. The experimental analysis considers three sets of comparisons to provide the platform to construct the metrics as a basis that can be used effectively to compare the scalability of software on cloud environments, consequently supporting deployment decisions with technical arguments. Moreover, the work integrates the technical scalability metrics with an earlier utility-oriented scalability metric. The third stage is a case study of application-level fault inection using real-world cloud-based software services running on Amazon EC2 cloud to demonstrate the effect of fault scenarios on the scalability behaviour. The results show that the technical metrics quantify explicitly the technical scalability performance of the cloud-based software services, and that they allow clear assessment of the impact of demand scenarios, cloud platform and fault injection on the software services’ scalability behaviour. The studies undertaken in this thesis have provided a valuable insight into the scalability of cloud-based software services delivery