Thanwadee Sunetnanta

Purit Phan-udom, Naruedon Wattanakul, Tattiya Sakulniwat et al.

Pythonic code is idiomatic code that follows guiding principles and practices within the Python community. Offering performance and readability benefits, Pythonic code is claimed to be widely adopted by experienced Python developers, but can be a learning curve to novice programmers. To aid with Pythonic learning, we create an automated tool, called Teddy, that can help checking the Pythonic idiom usage. The tool offers a prevention mode with Just-In-Time analysis to recommend the use of Pythonic idiom during code review and a detection mode with historical analysis to run a thorough scan of idiomatic and non-idiomatic code. In this paper, we first describe our tool and an evaluation of its performance. Furthermore, we present a case study that demonstrates how to use Teddy in a real-life scenario on an Open Source project. An evaluation shows that Teddy has high precision for detecting Pythonic idiom and non-Pythonic code. Using interactive visualizations, we demonstrate how novice programmers can navigate and identify Pythonic idiom and non-Pythonic code in their projects. Our video demo with the full interactive visualizations is available at https://youtu.be/vOCQReSvBxA.

Teeradaj Racharak, Chaiyong Ragkhitwetsagul, Chommakorn Sontesadisai et al.

In-context learning (ICL) has emerged as a powerful capability of large language models (LLMs), enabling them to perform new tasks based on a few provided examples without explicit fine-tuning. Despite their impressive adaptability, these models remain vulnerable to subtle adversarial perturbations and exhibit unpredictable behavior when faced with linguistic variations. Inspired by software testing principles, we introduce a software testing-inspired framework, called MMT4NL, for evaluating the trustworthiness of in-context learning by utilizing adversarial perturbations and software testing techniques. It includes diverse evaluation aspects of linguistic capabilities for testing the ICL capabilities of LLMs. MMT4NL is built around the idea of crafting metamorphic adversarial examples from a test set in order to quantify and pinpoint bugs in the designed prompts of ICL. Our philosophy is to treat any LLM as software and validate its functionalities just like testing the software. Finally, we demonstrate applications of MMT4NL on the sentiment analysis and question-answering tasks. Our experiments could reveal various linguistic bugs in state-of-the-art LLMs.

Saranphon Phaithoon, Supakarn Wongnil, Patiphol Pussawong et al.

Self-Admitted Technical Debt (SATD) is a special form of technical debt in which developers intentionally record their hacks in the code by adding comments for attention. Here, we focus on issue-related "On-hold SATD", where developers suspend proper implementation due to issues reported inside or outside the project. When the referenced issues are resolved, the On-hold SATD also need to be addressed, but since monitoring these issue reports takes a lot of time and effort, developers may not be aware of the resolved issues and leave the On-hold SATD in the code. In this paper, we propose FixMe, a GitHub bot that helps developers detecting and monitoring On-hold SATD in their repositories and notify them whenever the On-hold SATDs are ready to be fixed (i.e. the referenced issues are resolved). The bot can automatically detect On-hold SATD comments from source code using machine learning techniques and discover referenced issues. When the referenced issues are resolved, developers will be notified by FixMe bot. The evaluation conducted with 11 participants shows that our FixMe bot can support them in dealing with On-hold SATD. FixMe is available at https://www.fixmebot.app/ and FixMe's VDO is at https://youtu.be/YSz9kFxN_YQ.