Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

The field of Bug Reporting and Triaging has of late been a hot area of study among researchers trying to improve system management techniques. There is an increasing importance for developers to consider and address the various issues faced by users in order to not only ensure the delivery of quality service but also to understand the performance of the system under real-life scenarios. Hence, in this project, there is an attempt to develop a system that can improve the process of handling bug reports submitted by users of a software. This will be done through Bug Severity Prediction using the eXtreme Gradient Boosting (XGBoost) algorithm and the inclusion of a class balancing function to offset the bias due to the presence of majority and minority classes. The project would also include a study on the work that has already been done along with a proposal of the system architecture, methodologies used and the various hardware and software requirements. The main aim of the project is to shed light on the advantages of developing a Bug Severity Prediction system that can help reduce the dependence on users for providing accurate information. With the help of models built based on the history of bug reports received till date, the system should be able to take up some of the responsibilities of the user reporting the bug. © 2020 IEEE.

World University root node

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Proceedings of the 2020 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2020

Bug Severity Prediction System Using XGBoost Framework

VIT Unitversity

Fulltext

Journal of King Saud University - Computer and Information Sciences

The effect of 3D visualization on mainframe application maintenance: A controlled experiment

International Journal of Intelligent Systems and Applications

Automated Bug Assignment in Software Maintenance Using Graph Databases

Traceability Management plays a key role in tracing the artefacts associated with every requirement during the life cycle of a software project. However, the trace links between artefacts are not updated as the system evolves during the maintenance phase. This often leads to untrustworthy documentation and information gets scattered across a pile of untraceable documents that were created for various change management tasks. The goal of our research is on identifying an approach to trace requirements evolution across change requests and establish trace links between artifacts for such evolving requirements in the software maintenance phase. We have implemented a prototype Traceability Visualization Tool – VTrace for tracing requirements evolution. This tool also supports the visualization of trace links for evolving requirements. The effectiveness of the tool on change management tasks was tested using a controlled experiment. The results of our controlled experiment show that subjects who used the tool were 21% more accurate on change management tasks than subjects that didn’t use the tool. This study provides us with the evidence that tracing the evolution of software artefacts is highly significant for better system maintenance by novice engineers.

Department of Information Security

International Journal of Intelligent Engineering and Systems

The Impact of Visualizing Traceability Links for Evolving Requirements in Software Maintenance– A Controlled Experiment

Traceability Management plays a key role in tracing the life of a requirement through all the specifications produced during the development phase of a software project. A lack of traceability information not only hinders the understanding of the system but also will prove to be a bottleneck in the future maintenance of the system. Projects that maintain traceability information during the development stages somehow fail to upgrade their artefacts or maintain traceability among the different versions of the artefacts that are produced during the maintenance phase. As a result the software artefacts lose the trustworthiness and engineers mostly work from the source code for impact analysis. The goal of our research is on understanding the impact of visualizing traceability links on change management tasks for an evolving system. As part of our research we have implemented a Traceability Visualization Tool-VTrace that manages software artefacts and also enables the visualization of traceability links. The results of our controlled experiment show that subjects who used the tool were more accurate and faster on change management tasks than subjects that didn’t use the tool. © 2017, Institute of Advanced Engineering and Science. All rights reserved.

Bulletin of Electrical Engineering and Informatics

VTrace-a tool for visualizing traceability links among software artefacts for an evolving system

Background/Objectives: Software Documentation plays a significant role in any project. This paper is mainly focused on summarizing the various problems with documentation and the practices proposed to address them. Findings: The analysis establishes the fact that documentation needs are specific for each project. The documentation management issues relate to either neglecting or overdoing documentation. Application/Improvements: A study of all the issues and practices for documentation management revealed that documentation processes should be tailor made for every project according to the various factors that determine the documentation needs for a project. Implementation of documentation practices should be project specific rather than organization specific.

Indian Journal of Science and Technology

Software Documentation Management Issues and Practices: A Survey

An integrated Artificial Neural Network (ANN) approach to Short-Term Load Forecasting (STLF) is proposed in this paper. Four modules consisting of the Basic ANN, Peak and Valley ANN, Averager and Forecaster and Adaptive Combiner form the integrated method for load forecasting. The Basic ANN uses the historical data of load and temperature to predict the next 24h load, while the Peak and Valley ANN uses the past peak and valley data of load and temperatures, respectively. The Averager captures the average variation of the load from the previous load behaviour, while the adaptive combiner uses the weighted combination of outputs from the Basic ANN and the Forecaster, to forecast the final load. The regression based and time series methods are conceptually incorporated into the ANN to obtain an integrated load forecasting approach. © 2004 Elsevier B.V. All rights reserved.