SIZE AND PRODUCTIVITY METRICS BASED EFFORT ESTIMATION FOR SOFTWARE DEVELOPMENT

Abstract

Attaining accuracy in effort estimates can yield notable benefits in project planning and is crucial in facilitating efficient project management, frequently exhibiting a substantial correlation with the triumph of a software project. The research community has recently become increasingly interested in applying machine learning (ML) techniques for software effort estimation (SEE). Several researchers have employed different ML techniques for successful SEE over different software datasets. However, selecting the appropriate generalized model for a problem has proven challenging. Obtaining optimal outcomes in SEE by utilizing individual models poses significant challenges. Hence, the researchers proposed an alternate approach wherein multiple models (ensemble) are employed collectively to predict SEE. The ensemble models utilized in previous studies have treated the base learner’s hyperparameter tuning and weight assignment as separate entities, which may result in a trade-off between bias and variance, impacting the ensemble performance. So, we have proposed a SEE model based on the self-adaptive ensembling approach, integrating hyperparameter tuning and model weighting, considering the bias and variance trade-off in decision-making. Also, the SEE datasets contain heterogeneous projects with highly distributed effort values, which may also degrade the prediction model’s performance. So, we have developed a variant of self-adaptive ensembling based on locality to deal with the issues related to data heterogeneity. Accurate SEE is crucial for successfully implementing software projects, and software size plays a major role in it. However, previous methods for estimating effort were founded on metrics such as software lines of code or function points to estimate the size. The increasing need for additional functionalities and the incorporation of new features, such as software reuse, distributed systems, and iterative development, has necessitated the creation of new methodologies for estimating software size and effort. Additionally, previous metrics for software size and approaches for estimating software effort lack automation. They do not utilize Unified Modelling Language (UML) artifacts to reveal software features pertinent to software size. The UML diagrams automatically capture attributes pertinent to the computation of software size. The automation of extracting software size attributes from UML diagrams offers a more efficient approach to calculating software size and estimating software development effort. The Use Case Point (UCP) approach, established by Karner, is a widely recognized and significant early-stage SEE strategy founded on the fundamental elements of use case diagrams (actors and use cases). The utilization of UCP-based approaches is highly appropriate for this particular demand due to its advantageous alignment with two prominent industry practices: (1) the object-oriented (OO) development paradigm and (2) the utilization of use case modeling. Many researchers have conducted investigations utilizing several linear regressionbased models to estimate UCP. While the error estimates derived from existing models demonstrate improvement compared to traditional models, they cannot effectively handle nonlinear interactions within the UCP datasets. So, we have developed UCPbased models to estimate software efforts by utilizing different solo and ensemble models to handle nonlinear relationships in the SEE datasets. Also, the UCP approach consists of size estimation (in UCP) and effort estimation with calculated size. The productivity of a project is one of the main components for estimating effort from the UCP. However, productivity prediction is not explored in the UCP literature. So, we have also proposed a model for productivity prediction based on environmental factors.