Journal article 222 views
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 
,
Yogesh Dwivedi
,
Yogesh Dwivedi
Annals of Operations Research
Swansea University Authors:
Laurie Hughes , Yogesh Dwivedi
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...
| Published in: | Annals of Operations Research |
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| ISSN: | 0254-5330 1572-9338 |
| Published: |
Springer
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64058 |
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| 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 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. |
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| Keywords: |
Optimization models, Resource allocation decision model, Software code smells, Software reliability |
| College: |
Faculty of Humanities and Social Sciences |