Decision tree-based Design Defects Detection

Abstract

Design defects affect project quality and hinder development and maintenance. Consequently, experts need to minimize these defects in software systems. A promising approach is to apply the concepts of refactoring at higher level of abstraction based on UML diagrams instead of code level. Unfortunately, we find in literature many defects that are described textually and there is no consensus on how to decide if a particular design violates model quality. Defects could be quantified as metrics based rules that represent a combination of software metrics. However, it is difficult to find manually the best threshold values for these metrics. In this paper, we propose a new approach to identify design defects at the model level using the ID3 decision tree algorithm. We aim to create a decision tree for each defect. We experimented our approach on four design defects: The Blob, Data class, Lazy class and Feature Envy defect, using 15 Object-Oriented metrics. The rules generated using decision tree give a very promising detection results for the four open source projects tested in this paper. In Lucene 1.4 project, we found that the precision is 67% for a recall of 100%. In general, the accuracy varies from 49%, reaching for Lucene 1.4 project 80%.INDEX TERMS Anti-patterns, Bad smells, Decision tree, Model refactoring, Object oriented metrics.

A proposed model for detecting defects in software projects

“…The standard error (SE), the pseudo-R-squared (PRS), and the P-value (PV) are all crucial statistics in BLR. The SE is used to assess the accuracy of a sample distribution of the population [7]. The logistic regression model's power is shown via the usage of PRS [29].…”

“…There is a clear relationship between software quality and process maturity [7]. Many studies [8], [9] have stressed the need for inspections, especially requirements and design inspections, to limit faults' density.…”

Intelligent System to Detect Software Defects in Autonomous Cars

“…Programming languages such as C and C++ are not explored. [12] 2021 Detection of defects in design using decision tree approach.…”

“…Sometimes the software design can also cause blob, data class, lazy class, feature envy defect, and anti-patterns. Madden et al [12] proposed an Iterative Dichotomiser three decision tree model to detect software design defects. They have considered Unified Modeling Language (UML) diagrams instead of text codes and assigned one decision tree for each design defect.…”