Related papers: Empirical Study on the Software Engineering Practi…
Nowadays, machine learning (ML) is being used in software systems with multiple application fields, from medicine to software engineering (SE). On the one hand, the popularity of ML in the industry can be seen in the statistics showing its…
Software engineering (SE) is a dynamic field that involves multiple phases all of which are necessary to develop sustainable software systems. Machine learning (ML), a branch of artificial intelligence (AI), has drawn a lot of attention in…
Recent studies on software tool manipulation with large language models (LLMs) mostly rely on closed model APIs. The industrial adoption of these models is substantially constrained due to the security and robustness risks in exposing…
Powerful machine learning (ML) models are now readily available online, which creates exciting possibilities for users who lack the deep technical expertise or substantial computing resources needed to develop them. On the other hand, this…
Background: Machine Learning (ML) systems rely on data to make predictions, the systems have many added components compared to traditional software systems such as the data processing pipeline, serving pipeline, and model training. Existing…
Applying popular machine learning algorithms to large amounts of data raised new challenges for the ML practitioners. Traditional ML libraries does not support well processing of huge datasets, so that new approaches were needed.…
Logging is a common practice in traditional software development. Several research works have been done to investigate the different characteristics of logging practices in traditional software systems (e.g., Android applications, JAVA…
As easy-to-use deep learning libraries such as Tensorflow and Pytorch are popular, it has become convenient to develop machine learning models. Due to privacy issues with centralized machine learning, recently, federated learning in the…
The increasing reliance on applications with machine learning (ML) components calls for mature engineering techniques that ensure these are built in a robust and future-proof manner. We aim to empirically determine the state of the art in…
Machine-learning (ML) techniques have become popular in the recent years. ML techniques rely on mathematics and on software engineering. Researchers and practitioners studying best practices for designing ML application systems and software…
Clinical trials are conducted to test the effectiveness and safety of potential drugs in humans for regulatory approval. Machine learning (ML) has recently emerged as a new tool to assist in clinical trials. Despite this progress, there…
mlpack is an open-source C++ machine learning library with an emphasis on speed and flexibility. Since its original inception in 2007, it has grown to be a large project implementing a wide variety of machine learning algorithms, from…
Background. The development of empirical studies in software engineering mainly relies on the data available on code hosting platforms, being GitHub the most representative. Nevertheless, in the last years, the emergence of Machine Learning…
Large Language Models (LLMs) have become instrumental in advancing software engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. Like traditional SE tools, open-source collaboration is key in realising the…
Automated documentation of programming source code is a challenging task with significant practical and scientific implications for the developer community. We present a large language model (LLM)-based application that developers can use…
Large Language Models (LLMs) have recently demonstrated remarkable performance in various Natural Language Processing (NLP) applications, such as sentiment analysis, content generation, and personalized recommendations. Despite their…
While experimental reproduction remains a pillar of the scientific method, we observe that the software best practices supporting the reproduction of machine learning ( ML ) research are often undervalued or overlooked, leading both to poor…
Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine learning tasks to standardize the setup, execution, and…
The increasing inclusion of Machine Learning (ML) models in safety critical systems like autonomous cars have led to the development of multiple model-based ML testing techniques. One common denominator of these testing techniques is their…
Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting researchers. This overlooks practices and challenges in teaching, researching, or reviewing ML…