Abstract In the realm of software development, detecting code smells is a critical task for ensuring good code quality. God class code smell specifically has a specific nature associated with a great deal of subjectivity due to the levels of coupling and cohesion associated to it. Automated techniques for code smell detection aim to resolve this subjectivity. Machine learning techniques have shown promising results that tend to improve accuracy and reduce the bias associated with other techniques for God class identification. This is due to their pattern recognition capabilities making them more objective in identifying patterns that indicate code smells. However, current results need to be further improved in terms of both accuracy and generalizability. The challenge in the use of machine learning is not only in selecting the most appropriate technique but also lies in effectively representing source code as input patterns fed to Machine Learning (ML) classifier(s). Code representation plays a pivotal role in encoding source code for ML algorithms. This study aims improving the accuracy and generalizability of God class code smell detection via exploring the effect of using various code representation techniques, namely, tree-based, metric-based, code embedding, and token-based code representation techniques on the ML detection results. The study is conducted on the MLCQ dataset, and applies various ML algorithms (specifically: Logistic Regression, Random Forest, SVM, Decision Tree, Naive Bayes, Gradient Boosting, XGBoost). The evaluation results show how different code representation techniques influence ML detection outcomes and the comparative performance of ML algorithms. The study findings reveal that the F1-score achieved outperforms prior studies on the MLCQ dataset, indicating the effectiveness of the proposed approach. The presented results reveal how the code representation technique used makes a significant impact on the ML classifier results. This paves the way for developing intelligent IDE plugins for just in time God Class code smell detection among other code smells.

Despite the wide availability of automated testing techniques such as fuzzing, little attention has been devoted to testing computer architecture simulators. We propose a fully automated approach for this task. Our approach uses large language models (LLM) to generate input programs, including information about their parameters and types, as test cases for the simulators. The LLM’s output becomes the initial seed for an existing fuzzer, AFL++, which has been enhanced with three mutation operators, targeting both the input binary program and its parameters. We implement our approach in a tool called SearchSYS . We use it to test the gem5 system simulator. SearchSYS discovered 21 new bugs in gem5 , 14 where gem5 ’s software prediction differs from the real behaviour on actual hardware, and 7 where it crashed. New defects were uncovered with each of the 6 LLMs used.