Research Article

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Table 2

Estimation method and limitations.

Estimation methodLimitations

Estimation by analogySubjective selection of correlation standards and dispute identification process (confidence level)
Requires analogous project for comparison from historical data from database
These analogous projects are rarely available in software development

Decomposition and bottom-up (WBS-based)It may be time-consuming for large or even medium-sized projects
High risk of ignoring system-related tasks such as testing, integration, and configuration is high
This method may lead to underestimation due to lack of project information at early stage

Parametric models (SLIM, SEERSEM)Usually does not take into account the project team’s skill set specific to the organization’s software and project management culture
Modern methods of code reuse, code less programming, and various agile development methods for software development may not be feasible
Highly dependent on programming language

Expert estimation (Delphi, PERT, planning poker)These methods rely on the experience, knowledge, and perception of experts, and there may be deviations or biased, which often lead to overestimation or underestimation
All the factors used by experts in the estimation process are unable to justify and quantify

Size-based estimation models (use case, FPA, sTory points)Requires trained personnel which is not easily available
High effort and cost is required for the application of large projects
Due to limited information, using this method in the early stages of a project may result in inaccurate estimates