The paper presents a novel method to enhance software defect prediction accuracy using machine learning, focusing on feature selection to improve classifiers applied to public NASA datasets. It discusses existing challenges in defect prediction, including data quality issues and model interpretability, while proposing advanced feature extraction, hybrid models, and explainable AI for better integration within software development workflows. The study highlights advantages such as reduced false positives, improved developer productivity, and cross-project generalization in defect prediction models.

1. Base paper Title: A Novel Approach to Improve Software Defect Prediction Accuracy Using
Machine Learning
Modified Title: A New Method for Using Machine Learning to Increase the Accuracy of
Software Defect Prediction
Abstract
In software engineering community, defect prediction is one the active domain. For the
software’s success, it is essential to reduce the software engineering and data-mining gap.
Software defects prediction forecasts the source code errors before the testing phase. Methods
for predicting software defects, such as clustering, statistical methods, mixed algorithms,
metrics based on neural networks, black box testing, white box testing and machine learning
are frequently used to explore the effect area in software. The main contribution of this research
is the use of feature selection for the first time to increase the accuracy of machine learning
classifiers in defects pre-diction. The objective of this study is to improve the defects prediction
accuracy in five data sets of NASA namely; CM1, JM1, KC2, KC1, and PC1. These NASA
data sets are open to public. In this research, the feature selection technique is use with
machine-learning techniques; Random Forest, Logistic Regression, Multilayer Perceptron,
Bayesian Net, Rule ZeroR, J48, Lazy IBK, Support Vector Machine, Neural Networks, and
Decision Stump to achieve high defect prediction accuracy as compared to without feature
selection (WOFS). The research workbench, a machine-learning tool called WEKA (Waikato
Environment for Knowledge Analysis), is used to refine da-ta, preprocess data, and apply the
mentioned classifiers. To assess statistical analyses, a mini tab statistical tool is used. The
results of this study reveals that accuracy of defects prediction with feature selection (WFS) is
improve in contrast with the accuracy of WOFS.
Existing System
In software system, unexpected performance in response to a client’s need is known as
a defect. Software testers typically notice this unusual behavior in software. Software testers
notices errors in the software testing process. The term ‘‘software fault’’ is also use to describe,
‘‘irregularities in the software development process that frequently result in soft-ware failure
and fall short of user expectations’’ [1]. A defect is a lack of imperfection caused by an error,
fault, or failure in the software development process or product. According to the paradigm,

2. ‘‘error’’ refers to human behavior that leads to inappropriate out-comes, and ‘‘defect’’ refers
to a decision that leads to incorrect outcomes when trying to solve a problem. The process of
predicting software defects involves the detection of defected modules and a variety of testing
requirements. It is extremely difficult in software engineering to design a good defect
prediction model, which would predict malfunctioning software modules or software defects
in earlier phases of the software development life cycle. Re-viewing the source code, doing
beta testing, integration testing, system testing, and unit testing are all steps in the traditional
process of finding software errors. Therefore, it becomes challenging to carry out these tests as
software expands in size, complexity, and size of source code [2]. In recent years, software
defect prediction has become increasingly popular. Prediction of software defects has a direct
impact on software quality. Defective software modules have a significant impact on the
quality of the product, which causes price overruns, a delay in the software’s completion
timeframe, and increased maintenance costs [3]. The first and second fundamental methods of
software quality assurance are defect detection and defect prevention. The goal of defect
prevention is to stop potential faults as soon as possible. Defect prediction addressing current
flaws. According to Memon et al. [1], the process of enhancing software quality through defect
prevention is the focus of our research, which aims to increase software quality by forecasting
faults. Defect prevention activities include designing the algorithm, reviewing the execution of
the algorithm, and identifying errors in the planning of software need [4]. Prior to the software
product’s deployment process, the primary goal of defect prediction is to predict flaws, errors,
or defects in software products to anticipate the deliverable maintenance effort and quality [5].
The defect prevention approach is use to improve software quality [1]. Predicting errors is a
crucial step in creating good software. Because software deployment precedes defect prediction
to increase overall system performance and ensure user satisfaction.
Drawback in Existing System
 Data Quality Issues:
Imbalanced Data: Software defect datasets are often imbalanced, with a small number
of defective instances compared to non-defective ones. This can lead the model to be
biased towards the majority class.
Noisy Data: Real-world datasets may contain noise, errors, or irrelevant features that
can negatively impact the model's performance.

3.  Feature Selection and Engineering:
Identification of Relevant Features: Selecting and engineering relevant features is
crucial for the success of machine learning models. However, it can be challenging to
determine which features are most informative for predicting software defects.
 Model Interpretability:
Lack of Interpretability: Some machine learning models, especially complex ones like
deep neural networks, can be challenging to interpret. Understanding the rationale
behind a model's predictions is important, especially in safety-critical domains.
 Computational Resource Requirements:
High Computational Cost: Certain machine learning algorithms, particularly deep
learning models, may require significant computational resources for training and
inference. This can be a limitation in resource-constrained environments.
Proposed System
 Advanced Feature Extraction:
Implement advanced techniques for feature extraction from source code, such as code
complexity metrics, static code analysis, and dynamic analysis of code execution paths.
Consider using natural language processing (NLP) for analyzing comments and
documentation.
 Hybrid Rule-Based and Data-Driven Models:
Develop a hybrid model that combines traditional rule-based approaches with
machine learning models. This approach allows explicit rules to be incorporated while
leveraging the data-driven nature of machine learning.

4.  Explainable AI (XAI):
Emphasize the importance of model interpretability. Use techniques from
Explainable AI to provide insights into the decision-making process of the machine
learning model, enhancing trust and understanding.
 Integration with Development Workflow:
Integrate the defect prediction model into the software development workflow
seamlessly. Develop plugins or extensions for popular integrated development
environments (IDEs) to ensure accessibility for developers.
Algorithm
 Advanced Feature Engineering:
Develop innovative ways to extract features from code, such as code complexity
metrics, code churn, and developer collaboration patterns.
Utilize natural language processing techniques to analyze comments and
documentation for additional context.
 Explainable AI (XAI):
Focus on developing models that are interpretable and provide explanations for their
predictions. This is crucial for gaining trust from software developers and stakeholders.
 Domain-Specific Knowledge Integration:
Collaborate closely with domain experts and software developers to incorporate
domain-specific knowledge into the model development process. This can lead to more
informed feature selection and model design.
Advantages
 Reduced False Positives:
Novel approaches may be designed to minimize false positive predictions, ensuring
that developers focus on genuine software defects rather than spending time on false
alarms. This helps in improving the efficiency of the development process.

5.  Enhanced Feature Representation:
Advanced feature engineering techniques can provide a more nuanced and
comprehensive representation of the software code. This can lead to a better
understanding of the factors contributing to defects, improving the model's predictive
capabilities.
 Improved Developer Productivity:
Early and accurate defect predictions can lead to improved developer productivity by
enabling them to focus on critical areas of the code that are more likely to contain
defects. This targeted approach can streamline the debugging and testing process.
 Cross-Project Generalization:
Approaches that consider cross-project defect prediction can lead to models that
generalize well across different projects. This can be particularly advantageous when
dealing with limited labeled data for a specific project.
Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB
Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm