Estimation of Defect proneness Using Design complexity Measurements in Object- Oriented Software

TL;DR

This paper presents a novel methodology using CK metrics to predict defect proneness in object-oriented software at the design stage, aiming to improve quality and maintainability.

Contribution

It introduces a new estimation model linking CK metrics to defect-proneness, enhancing early detection and quality control in software design.

Findings

CK metrics correlate with defect-proneness levels

The model improves early defect prediction accuracy

Supports better planning and resource allocation

Abstract

Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and methods which can enable a more reusable, reliable, easily maintainable and high quality software systems with deeper control on software generation process. Quality and productivity are indeed the two most important parameters for controlling any industrial process. Implementation of a successful control system requires some means of measurement. Software metrics play an important role in the management aspects of the software development process such as better planning, assessment of improvements, resource allocation and reduction of unpredictability. The process involving early detection of potential problems, productivity evaluation and evaluating external quality factors such as reusability, maintainability, defect proneness and complexity are of utmost importance. Here we discuss the application of CK metrics and estimation model to predict the external quality parameters for optimizing the design process and production process for desired levels of quality. Estimation of defect-proneness in object-oriented system at design level is developed using a novel methodology where models of relationship between CK metrics and defect-proneness index is achieved. A multifunctional estimation approach captures the correlation between CK metrics and defect proneness level of software modules.