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Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System

Priyanka Gupta, Adarsh Anand , Ronnie Das, Laurie Hughes Orcid Logo, Yogesh Dwivedi Orcid Logo

Annals of Operations Research

Swansea University Authors: Laurie Hughes Orcid Logo, Yogesh Dwivedi Orcid Logo

  • Accepted Manuscript under embargo until: 25th August 2024

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DOI (Published version): 10.1007/s10479-023-05536-7

Abstract

The art of business process optimization and resolution through advanced analytics have gained popularity across all business sectors in recent years. An emerging stream of modern analytics methods have focused on analyzing software development firms and their approach to coding and software develop...

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Published in: Annals of Operations Research
ISSN: 0254-5330 1572-9338
Published: Springer
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URI: https://cronfa.swan.ac.uk/Record/cronfa64058
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spelling v2 64058 2023-08-08 Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System 7abaa0ecff88cdfd7a208d27a8b62173 0000-0002-0956-0608 Laurie Hughes Laurie Hughes true false d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2023-08-08 BBU The art of business process optimization and resolution through advanced analytics have gained popularity across all business sectors in recent years. An emerging stream of modern analytics methods have focused on analyzing software development firms and their approach to coding and software development, with a view to optimize key processes through data to enhance organizational value. Despite systematic developments within the field, many high throughput software suffers from incomprehensible bad program structure that results in imbalanced performance throughout its lifecycle. This phenomenon is widely known as “Code Smells, i.e., design flaws”. A series of AI and ML based algorithms and mathematical tools were developed to tackle the problem in the past. However, at the business and decision-making level there are large uncertainties on how to optimize resource allocation towards each code smell class to maximize benefit of the process. In this paper we propose a novel mathematical business model that will help business and operational managers to optimize budget and resource allocation towards detection of maximum number of smells within a system, with increased output efficiency. Our proposed model benefits from a real life validation of code smell dataset, along with detailed prescription of optimal resource allocation along with reasoning. Journal Article Annals of Operations Research Springer 0254-5330 1572-9338 Optimization models, Resource allocation decision model, Software code smells, Software reliability 0 0 0 0001-01-01 10.1007/s10479-023-05536-7 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2023-09-11T16:51:38.6362704 2023-08-08T17:39:08.5046961 Faculty of Humanities and Social Sciences School of Management - Business Management Priyanka Gupta 1 Adarsh Anand 2 Ronnie Das 3 Laurie Hughes 0000-0002-0956-0608 4 Yogesh Dwivedi 0000-0002-5547-9990 5 Under embargo Under embargo 2023-09-11T16:46:26.2132026 Output 357543 application/pdf Accepted Manuscript true 2024-08-25T00:00:00.0000000 true eng
title Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System
spellingShingle Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System
Laurie Hughes
Yogesh Dwivedi
title_short Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System
title_full Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System
title_fullStr Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System
title_full_unstemmed Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System
title_sort Developing Algorithmic Business Resource Optimization Model for Code Smells Detection: An Applied Case Insight from Enterprise Level Software Management System
author_id_str_mv 7abaa0ecff88cdfd7a208d27a8b62173
d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv 7abaa0ecff88cdfd7a208d27a8b62173_***_Laurie Hughes
d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Laurie Hughes
Yogesh Dwivedi
author2 Priyanka Gupta
Adarsh Anand
Ronnie Das
Laurie Hughes
Yogesh Dwivedi
format Journal article
container_title Annals of Operations Research
institution Swansea University
issn 0254-5330
1572-9338
doi_str_mv 10.1007/s10479-023-05536-7
publisher Springer
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 0
active_str 0
description The art of business process optimization and resolution through advanced analytics have gained popularity across all business sectors in recent years. An emerging stream of modern analytics methods have focused on analyzing software development firms and their approach to coding and software development, with a view to optimize key processes through data to enhance organizational value. Despite systematic developments within the field, many high throughput software suffers from incomprehensible bad program structure that results in imbalanced performance throughout its lifecycle. This phenomenon is widely known as “Code Smells, i.e., design flaws”. A series of AI and ML based algorithms and mathematical tools were developed to tackle the problem in the past. However, at the business and decision-making level there are large uncertainties on how to optimize resource allocation towards each code smell class to maximize benefit of the process. In this paper we propose a novel mathematical business model that will help business and operational managers to optimize budget and resource allocation towards detection of maximum number of smells within a system, with increased output efficiency. Our proposed model benefits from a real life validation of code smell dataset, along with detailed prescription of optimal resource allocation along with reasoning.
published_date 0001-01-01T16:51:40Z
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score 11.014358

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