相关论文: Error Identification Strategies for Python Jupyter…
In the Jupyter ecosystem, data visualization is usually done with "widgets" created as notebook cell outputs. While this mechanism works well in some circumstances, it is not well-suited to presenting interfaces that are long-lived,…
Computational notebooks (e.g., Jupyter, Google Colab) are widely used for interactive data science and machine learning. In those frameworks, users can start a session, then execute cells (i.e., a set of statements) to create variables,…
At present, code recommendation tools have gained greater importance to many software developers in various areas of expertise. Having code recommendation tools has enabled better productivity and performance in developing the code in…
Visual programs are executable code generated by large language models to address visual reasoning problems. They decompose complex questions into multiple reasoning steps and invoke specialized models for each step to solve the problems.…
Currently, programming instructors continually face the problem of helping to debug students' programs. Although there currently exist a number of debuggers and debugging tools in various platforms, most of these projects or products are…
Recent work identified clarity as one of the top quality attributes that notebook users value, but notebooks lack support for maintaining clarity throughout the exploratory phases of the notebook authoring workflow. We propose always-clear…
Variable names are important to understand and maintain code. If a variable name and the value stored in the variable do not match, then the program suffers from a name-value inconsistency, which is due to one of two situations that…
We report a user-friendly software environment for battery data science. It is designed to streamline data management, data cleaning, and data analysis to help bridge the gap between the domain expertise of most battery scientists and the…
We investigate sketch-like pen input as an alternative way to support execution control in interactive debugging. In our interface, programmers draw lightweight marks to set breakpoints, use symbolic strokes to control execution, and extend…
Recent advancements in quantum computing software are gradually increasing the scope and size of quantum programs being developed. At the same time, however, these larger programs provide more possibilities for functional errors that are…
A type debugger interactively detects the expressions that cause type errors. It asks users whether they intend the types of identifiers to be those that the compiler inferred. However, it seems that novice programmers often get in trouble…
Grading student assignments in STEM courses is a laborious and repetitive task for tutors, often requiring a week to assess an entire class. For students, this delay of feedback prevents iterating on incorrect solutions, hampers learning,…
Recently, detection of label errors and improvement of label quality in datasets for supervised learning tasks has become an increasingly important goal in both research and industry. The consequences of incorrectly annotated data include…
Dynamically typed programming languages are popular in education and the software industry. While presenting a low barrier to entry, they suffer from run-time type errors and longer-term problems in code quality and maintainability.…
Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several…
With the advent of Open Science, researchers have started to publish their research artefacts (i. e., data, software, and other products of the investigations) in order to allow others to reproduce their investigations. While this…
Kubernetes is a tool that facilitates rapid deployment of software. Unfortunately, configuring Kubernetes is prone to errors. Configuration defects are not uncommon and can result in serious consequences. This paper reports an empirical…
Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and…
Large language models (LLMs) have shown great promise in automating data science workflows, but existing models still struggle with multi-step reasoning and tool use, which limits their effectiveness on complex data analysis tasks. To…
The quality of scientific code is a critical concern for the research community. Poorly written code can result in irreproducible results, incorrect findings, and slower scientific progress. In this study, we evaluate scientific code…