Search Results for author:
Found 12 papers, 5 papers with code
Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers
We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning.
A State-of-the-practice Release-readiness Checklist for Generative AI-based Software Products
This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readiness for release.
An Empirical Study of Challenges in Machine Learning Asset Management
In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle.
Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware
Finally, we (i) show how the unique properties of FMArts enabled us to design and develop a complex FMware for a large customer in a timely manner and (ii) discuss the lessons that we learned in doing so.
Studying the Practices of Testing Machine Learning Software in the Wild
Aims: To fill this gap, we perform the first fine-grained empirical study on ML testing practices in the wild, to identify the ML properties being tested, the followed testing strategies, and their implementation throughout the ML workflow.
Multi-Granularity Detector for Vulnerability Fixes
To address these challenges and boost the effectiveness of prior works, we propose MiDas (Multi-Granularity Detector for Vulnerability Fixes).
An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks
In this paper, we present a qualitative and quantitative analysis of the most frequent dl libraries combination, the distribution of dl library dependencies across the ml workflow, and formulate a set of recommendations to (i) hardware builders for more optimized accelerators and (ii) library builder for more refined future releases.
The Impact of Using Regression Models to Build Defect Classifiers
We find that: i) Random forest based classifiers outperform other classifiers (best AUC) for both classifier building approaches; ii) In contrast to common practice, building a defect classifier using discretized defect counts (i. e., discretized classifiers) does not always lead to better performance.
Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering
Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e. g., median).
The impact of feature importance methods on the interpretation of defect classifiers
We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods).
What Makes a Popular Academic AI Repository?
Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers.
Software Engineering
CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words
CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool, and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query.
