Artificial Intelligence (AI) techniques, especially Large Language Models (LLMs), have started gaining popularity among researchers and software developers for generating source code. However, LLMs have been shown to generate code with quality issues and also incurred copyright/licensing infringements. Therefore, detecting whether a piece of source code is written by humans or AI has become necessary. This study first presents an empirical analysis to investigate the effectiveness of the existing AI detection tools in detecting AI-generated code. The results show that they all perform poorly and lack sufficient generalizability to be practically deployed. Then, to improve the performance of AI-generated code detection, we propose a range of approaches, including fine-tuning the LLMs and machine learning-based classification with static code metrics or code embedding generated from Abstract Syntax Tree (AST). Our best model outperforms state-of-the-art AI-generated code detector (GPTSniffer) and achieves an F1 score of 82.55. We also conduct an ablation study on our best-performing model to investigate the impact of different source code features on its performance.

Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level and, more recently, in the design and coding phases of the software development life cycle. However, software testing’s impact on energy consumption did not receive attention from the research community. Specifically, how test code design quality and test smell (e.g., sub-optimal design and bad practices in test code) impact energy consumption has not been investigated yet. This study aims to examine open-source software projects to analyze the association between test smell and its effects on energy consumption in software testing. We conducted a mixed-method empirical analysis from two perspectives; software (data mining in 12 Apache projects) and developers’ views (a survey of 62 software practitioners). Our findings show that: (1) test smell is associated with energy consumption in software testing. Specifically, the smelly part of a test case consumes more energy compared to the non-smelly part. (2) certain test smells are more energy-hungry than others, (3) refactored test cases tend to consume less energy than their smelly counterparts, and (4) most developers (45 % of the survey respondents) lack knowledge about test smells’ impact on energy consumption. Based on the results, we emphasize raising developers awareness regarding the impact of test smells on energy consumption. Additionally we present several observations that can direct future research and developments.