Report on Current Developments in Software Engineering: AI-Assisted Big Models and Data-Driven Approaches
General Direction of the Field
The field of software engineering (SE) is currently witnessing a significant shift towards integrating artificial intelligence (AI) and machine learning (ML) methodologies with traditional model-driven software engineering (MDSE) practices. This convergence aims to leverage the strengths of both approaches to enhance software development processes, improve software quality, and automate coding tasks. The emergence of "big code" datasets, sourced from open-source platforms, is driving empirical software engineering forward, enabling more robust and scalable models.
One of the primary trends is the development of AI-assisted big models, which combine the structured, domain-specific knowledge of MDSE with the data-driven insights of AI. This hybrid approach seeks to reduce the manual effort required for model development and maintenance, while also enhancing the adaptability and scalability of software systems. The proposed paradigm of "pair modelling" in MDSE exemplifies this trend, suggesting a collaborative approach where AI and human expertise work in tandem to create more effective software models.
Another notable development is the increasing emphasis on data-driven methodologies in SE research. The success of the mining software repositories (MSR) community has paved the way for large-scale code mining and pre-processing tools, which are essential for training and validating deep learning models. These tools are becoming more accessible and user-friendly, allowing researchers to quickly build and pre-process datasets tailored to their specific needs, thereby reducing the time and computational resources required for empirical studies.
Furthermore, there is a growing focus on creating representative samples of software repositories to ensure that empirical studies are based on data that accurately reflects the broader population of software projects. This is particularly important as the availability of data from social coding platforms like GitHub continues to expand. Researchers are developing more sophisticated sampling techniques that align with the characteristics of the repository population and the requirements of the empirical study, thereby enhancing the validity and reliability of their findings.
Noteworthy Papers
Next-Gen Software Engineering: AI-Assisted Big Models: This paper introduces a visionary synthesis of AI and MDSE, proposing a new paradigm of pair modelling that could revolutionize software development practices.
SEART Data Hub: Streamlining Large-Scale Source Code Mining and Pre-Processing: The SEART Data Hub significantly reduces the time and resources required for dataset creation, making large-scale code mining more accessible to researchers.
On the Creation of Representative Samples of Software Repositories: This paper presents a novel methodology for creating representative samples, which is crucial for ensuring the validity of empirical studies in software engineering.
GEMS: Generative Expert Metric System through Iterative Prompt Priming: GEMS demonstrates the potential of AI in transforming theories into context-aware metrics, offering a framework that could be widely applicable across various fields.
