Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality…

Defect prediction models aim to identify parts of the software that are likely to contain defects by

analyzing historical data and code quality metrics. The primary goal is to use this predictive

information to allocate testing and maintenance resources effectively. Let's break down why option

D is the correct choice:

Understanding Classification Models:

Classification models are a type of supervised learning algorithm used to categorize or classify data

into predefined classes or labels. In the context of defect prediction, the classification model would

classify parts of the code as either "defective" or "non-defective" based on the input features.

Input Data - Code Quality Metrics:

The input data for these classification models typically includes various code quality metrics such as

cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between

objects, etc. These metrics help the model learn patterns associated with defects.

Historical Data:

Historical versions of the code along with their defect records provide the labeled data needed for

training the classification model. By analyzing this historical data, the model can learn which metrics

are indicative of defects.

Why Option D is Correct:

Option D specifies using a classification model to predict the presence of defects by using code

quality metrics as input data. This accurately describes the process of defect prediction using

historical bug data and quality metrics.

Eliminating Other Options:

A . Identifying the relationship between developers and the modules developed by them: This does

not directly involve predicting defects based on code quality metrics and historical data.

B . Search of similar code based on natural language processing: While useful for other purposes, this

method does not describe defect prediction using classification models and code metrics.

C . Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised

learning technique and does not directly align with the supervised learning approach typically used

in defect prediction models.

Reference:

ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques

including classification models for defect prediction.

"Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).