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

Risk management is considered as a critical project management activity that needs to be performed for successful software development. Within risk management, risk prioritization is an important process which helps the software team to effectively manage the risks at early stage of the project. In agile-based software environment, it is necessary to prioritize the risks in an effective manner in order to address the risks in shorter duration of time. In recent times, swarm intelligence techniques are widely popular in solving various optimization problems in software development process. The main reason is due to its convergence accuracy toward global optimal solution and faster computational time. In this study, an efficient risk prioritization technique termed as ARP–GWO (agile risk prioritization–grey wolf optimization) has been proposed for prioritizing the risk factors present in the agile software development using grey wolf optimization (GWO). The proposed ARP–GWO method helps the organization to mitigate the risks and ensures successful delivery of software products with good quality, in lesser cost and time. The effectiveness of ARP–GWO is analyzed using two performance metrics, namely Index of Integration and Usability Goals Achievement Metric, for which case studies are performed on five industrial projects from two different organizations. The experimental results indicate that ARP–GWO is most effective in prioritization of risks and offers better enhancement with high degree of satisfaction among developers and users as compared with the existing agile process. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

Computer Science

School of Computer Science and Engineering

Chennai Campus

Soft Computing

ARP–GWO: an efficient approach for prioritization of risks in agile software development

Risks are in general termed as threats or uncertainties that influence the project performance and its outcomes to the greater extent. To ensure software quality and project success, every organisation should enforce a proper mechanism to efficiently manage the risks irrespective of the development model they follow. Risk prioritisation is a most critical step in risk management process that helps the organisation to resolve the risks in shorter duration of time. In this paper, a comparative study about different meta-heuristic optimisation techniques for prioritising the risks in agile environments is presented. The five most effective meta-heuristic optimisation algorithms such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Grey Wolf Optimisation (GWO) and Analytical Hierarchy Process (AHP) are considered and the results are evaluated based on four key criterion attributes such as error rate, accuracy, reliability, and running time. The result proves that GWO outperforms other four meta-heuristic optimisation techniques for the prioritisation of risks in agile environment. Copyright © 2020 Inderscience Enterprises Ltd.

International Journal of Computer Applications in Technology

A comparative study of meta-heuristic optimisation techniques for prioritisation of risks in agile software development

Software testing is considered as one of the most challenging and time consuming activity involved in the software development process. Testing involves the creation of test cases that helps to explore the defects present in the software system. Test cases play a major role in the testing process to uncover the critical errors that exist in the system. Thus, it is necessary to prioritize test cases for effective testing. This paper aims at performing a comparative study for the optimization of test cases involved in software development using Meta-heuristic techniques. The weightage of each test case is determined,which helps in optimizing the test cases. The performance is evaluated by comparing two meta-heuristic algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).The result shows PSO outperforms GA in terms of accuracy, error rate, and execution time for the chosen test case optimization problem. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

World University root node

International Journal of Advanced Trends in Computer Science and Engineering

Optimization of test cases: A meta-heuristic approach

Software development is a challenging process that requires in-depth understanding and an effective model such that the developed software inherits good quality and reliability, and attains customer satisfaction towards achieving the goals successfully. The effectiveness of the software is enabled by modifying the operating modules of the software through a model, like agility. In this paper, the catastrophic and distributed computing models are integrated into the software development process. The proposed model is termed as a distributed cat model that is developed with the aim to handle the risk factors engaged in various developing stages of the agile model. The risk factors that affect the communication, planning, release, design, coding and testing modules of the agile modules are deeply learned and executed such that the risk factors are tackled by various modules present in the proposed distributed cat model. The effectiveness of the proposed model is analysed based on the performance metrics such as Index of Integration (IoI) and Usability Goals Achievement Metric (UGAM), for which five products, including the hotel management system, Customer Relationship Management system (CRM), rainfall prediction system, temperature monitoring system and meta-search system, are employed. The analysis is performed using the parameters like mean difference, variance, standard deviation and correlation coefficient. The result proves that the proposed model offers a great positive deviation contributing to high degree of performance in software development. © 2019, Indian Academy of Sciences.

Fulltext

Sādhanā

Distributed cat modeling based agile framework for software development

As social media is in boom, it is becoming very easier for customers to share their views and comments and express their feelings regarding any products which are present in online social media. . If these data can be analyzed efficiently different suggestions can be provided to the company regarding to improvise their products sale. It becomes easier for the company to understand the customer’s reaction after seeing the advertisements of the products posted on social media. This research focuses on analyzing the sentiments of customers based on the comments and reviews of products available in Facebook. Sentimental Analysis is performed to analyze the customer comments as positive, negative and neutral and later they are labeled as 0 or 1. After the labeling process, a comparative analysis is performed using different classification algorithms. The classification algorithms used are K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naïve Bayes Classifier. The classification algorithm with the highest accuracy is identified to predict the sales of online products

VIT Unitversity

International Journal of Recent Technology and Engineering 2

Using Data Mining Techniques to Analyze the Customers Reaction towards Social Media Advertisements

Risks are uncertainties that influence the project performance to greater extent. To ensure software quality and project success every organizations should enforce a proper mechanism to efficiently manage the risks irrespective of the process model they follow. Risk prioritization is a most critical step in risk management process that helps the organization to resolve the risks in shorter duration of time. This study focuses on prioritizing the risks involved in software development using Grey Wolf Optimization (GWO) algorithm. Further, the proposed Software Risk Prioritization (SRP-GWO) approach is compared with other prioritization techniques such as Analytical Hierarchy Process (AHP), Particle Swarm Optimization (PSO), Delphi, Average Ranking and Categorizing scale and the results are evaluated based on five criterion attributes such as Simplicity, Adaptability, Running time, Accuracy and Consistency. The findings show that the proposed approach outperforms other existing techniques. © BEIESP.

International Journal of Recent Technology and Engineering

Risk prioritization for software development using grey wolf optimization

In recent years many software organizations started adopting agile processes for developing the software product in order to deliver better quality product in lesser cost and duration. Most of the client prefers incorporating agile approaches in their product development. The major reasons behind this are higher customer satisfaction, short releases, frequent feedback and ability to incorporate requirement changes even in the later stage of the development. Like any software projects, risks are most common factor exists even in agile based projects which might determine the success or failure of software development. This paper focuses on managing the risk factors that influence the performance of agile development using Ontology model. The study is based on the inputs derived from various sources like conducting in-depth interviews among software practitioners, analysis of twenty six various completed project documents from ten different software Organizations and various professional bodies such as PMBOK, SWEBOK and IEEE Computer Society. Based on the inputs the risks are categorized and risk ontology is created. This automated approach benefits the users to identify the possible risks for a given project, track the sources of risks and its dependency among them and also helps to efficiently manage the risks. The proposed model has been evaluated based on the two performance metrics, Usability Goals Achievement Metric (UGAM) and Index of Integration (IoI). The analysis proves that the proposed model achieved better results in terms of satisfaction level among product users and software professionals. © 2018, Institute of Advanced Scientific Research, Inc.. All rights reserved.

Journal of Advanced Research in Dynamical and Control Systems

Ontology based risks management model for agile software development

Software projects mostly exceeds budget, delivered late and does not meet with the customer’s satisfaction for years. In the past, many traditional development models like waterfall, spiral, iterative, and prototyping methods are used to build the software systems. In recent years, agile models are widely used in developing the software products. The major reasons are – simplicity, incorporating the requirement changes at any time, light-weight approach and delivering the working product early and in short duration. Whatever the development model used, it still remains a challenge for software engineer’s to accurately estimate the size, effort and the time required for developing the software system. This survey focuses on the existing estimation models used in traditional as well in agile software development.

Indonesian Journal of Electrical Engineering and Computer Science

A Survey on Software Estimation Techniques in Traditional and Agile Development Models

Frequent pattern mining algorithms as the name says, mines sets frequent patterns form given datasets. These algorithms provide immensely helpful results which have a wide scope of application starting from simple decision problems to complex business intelligence aspects. This paper attempts to apply the same concept of frequent pattern mining to solve the location management problem of GSM networks. In GSM networks the task of keeping track of a mobile user (MU) and relaying an incoming call, is called location management. It basically includes two processes, location update and paging. Location update deals with managing the current location of the MUs. There are many approaches to do this like time based, movement based, distance based etc. In this paper the location update procedure relies on the collected data of user movements form one network cell to another cell. This data has a definite pattern, as in day to day life a person mostly has a fixed route of travelling e.g. home to office in the morning and office to home in the evening. During this movement he crosses a specific set of network cells which remains same throughout the week. Thus, frequent pattern mining algorithm can be applied on the user’s mobility log and try to find out the most probable location where the mobile user could be found. Using the results, a dynamic location area for individual user’s current location can be created. Thus minimizing cost related to location update which otherwise involves communication between the mobile handset and the base station, and calculations related to keeping track of the location of mobile users.

Asian Journal of Pharmaceutical and Clinical Research

DYNAMIC LOCATION AREA PLANNING IN CELLULAR NETWORK USING FREQUENT PATTERN MINING

Indian Journal of Science and Technology

Analysis of University Students Travel Behaviour: En Route to Sustainable Campus

The aim of this research was to analyse and experience the various electronics company profiles in various countries using giant social media, Facebook. This analysis was performed with the Insight data of Facebook's page which provide 4 different count values named day, day-28, week and lifetime respectively. To analyze the company's performance in various countries, aggregation was performed to find total users in a country those are engaged with different Facebook pages. All these four counts were used to compare various companies popularity using various measures like Total Country in which people knows about Company, Top-K Country and Least-K country, Count Comparison, Country wise Standard Deviation, Correlation between two companies in a country. Analysis results proved that Samsung was more popular in most of the country compared to all other companies. These findings will definitely help the companies in improving their popularity in social media, which intern will improve their business. © 2015 The Authors. Published by Elsevier B.V.