Using NLP to Automate Intrinsic Bug Identification in Software Development

In their April 2025 paper titled Buggin, Pragya Bhandari and Gema Rodríguez-Pérez introduce an innovative model that automatically classifies intrinsic software bugs using NLP and machine learning techniques.

The study addresses the lack of automated methods to identify intrinsic bugs in software by applying NLP techniques to bug reports. Using seBERT and TF-IDF embeddings from bug titles and descriptions, the research evaluates machine learning algorithms including Decision Trees, SVMs, and Random Forests. The highest F1 scores for identifying intrinsic bugs were achieved with TF-IDF combined with Decision Trees (78%) and seBERT with SVMs (77%). This demonstrates an effective approach to automate intrinsic bug detection using textual data from bug reports.

Machine learning (ML) has emerged as a transformative force in software engineering, reshaping how developers identify, predict, and resolve bugs. By leveraging advanced algorithms and data-driven insights, ML is enabling more accurate predictions about potential issues, improving code quality, and streamlining the development process. This article explores how machine learning is revolutionizing software engineering, focusing on its applications in bug prediction, issue classification, and natural language processing (NLP).

Bug Prediction: Anticipating Issues Before They Arise

One of the most significant contributions of ML to software engineering is its ability to predict bugs before they manifest. Researchers have developed sophisticated models that analyze historical data to identify patterns associated with bug-inducing changes. For instance, studies have shown that certain code modifications are more likely to introduce errors, and ML algorithms can flag these changes during development.

Moreover, recent advancements in parameter optimization have enhanced the accuracy of defect prediction models. By automating the selection of optimal parameters, these models achieve higher precision, reducing false positives and ensuring developers focus on genuine risks. This not only improves software quality but also accelerates the development cycle by addressing issues early.

However, challenges remain. For example, extrinsic bugs—those introduced indirectly through dependencies or external libraries—pose unique difficulties for prediction models. Addressing these requires a more holistic approach, integrating insights from diverse data sources to capture the full spectrum of potential risks.

Issue Classification: Streamlining Problem Resolution

Another critical application of ML in software engineering is issue classification. Platforms like GitHub host millions of bug reports and feature requests, creating a vast repository of unstructured data. Classifying these issues manually is time-consuming and error-prone, but ML algorithms can automate this process with remarkable efficiency.

Techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) and ensemble modeling have proven particularly effective in categorizing issues across projects. By analyzing the textual content of bug reports, these methods identify patterns and assign labels that guide developers to the most critical problems. This not only saves time but also ensures that resources are allocated effectively, improving overall productivity.

Natural Language Processing: Bridging Human and Machine Communication

NLP has emerged as a game-changer in software engineering by enabling machines to understand human language. Tools like BERT (Bidirectional Encoder Representations from Transformers) have been adapted to analyze developer discussions, code comments, and bug reports, providing deeper insights into the development process.

For example, NLP can identify subtle linguistic cues that indicate frustration or confusion among developers, signaling potential issues before they escalate. Additionally, by analyzing commit messages and pull requests, ML models can detect patterns associated with successful or problematic changes, offering actionable recommendations to improve code quality.