Effectiveness of lazy algorithm-based software effort estimation
*ShaluCorresponding authorsingshalu2609@gmail.comDepartment of Computer Science & TechnologyManav Rachna UniversityFaridabad, Haryana, 121004, India0000-0002-5516-0357View full profile → , Neha Sainiprofnehasaini@gmail.comDepartment of Computer ScienceGovernment College, Chhachhrauli (Yamuna Nagar)Chhachhrauli, Yamuna Nagar, Haryana, 135001, IndiaView full profile → , Aman Kumaraman.kec@gmail.comDepartment of Computer Science EngineeringSchool of Engineering & TechnologyManav Rachna International Institute of Research and StudiesFaridabad, Haryana, 121004, IndiaView full profile → , Ankit Gambhirer.ankit.gambhir@gmail.comDepartment of Computer Science & EngineeringTrinity Institute of Professional StudiesGuru Gobind Singh Indraprastha UniversityGreater Noida, Uttar Pradesh, 201310, IndiaView full profile → , Aniket Singhaniketsingh@mru.edu.inDepartment of Computer Science & TechnologyManav Rachna UniversityFaridabad, Haryana, 121004, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2026
- Published Online:
- 31 Jul 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2332
- Pages:
- 2779–2788
Abstract
Effort estimation in software development is crucial, since it enables a company to distribute resources efficiently, establish project schedules, and formulate budgets. Conventional approaches to the quantification of software effort occasionally rely on complex models and a detailed pre-processing of data, which makes them very resource-consuming and time-intensive. This research paper examines the feasibility of lazy algorithms as a feasible alternative method of assessing software effort in real life context. The main goal is to improve the estimating process, but maintain accuracy. The use of relaxed algorithms, including case-based reasoning and instance-based learning methods offers a distinct way of predicting software development effort. These strategies utilise real-time data with minimum pre-processing, hence providing an advantageous estimation strategy. Lazy algorithms are the ones that could quickly adjust to emerging projects and enhance the precision of forecasts as the data expands through the use of past cases and the related results. Proposed study findings indicate that it is possible to achieve the same accuracy results with lazy algorithms as with more complex models, and requires less data preprocessing and model complexity. This shows that the lazy algorithms can become a potentially successful way of estimating software effort. Out of two lazy algorithms i.e. K* and IBL empirically evaluated in this research paper, Lazy IBL has shown impressive results for three datasets i.e. NASA, Heiat Heiat and Desharnais out of four used in this research study.
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References
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