Background: Meeting the growing industry demand for Data Science requires cross-disciplinary teams that can translate machine learning research into production-ready code. Software engineering teams value adherence to coding standards as an indication of code readability, maintainability, and developer expertise. However, there are no large-scale empirical studies of coding standards focused specifically on Data Science projects. Aims: This study investigates the extent to which Data Science projects follow code standards. In particular, which standards are followed, which are ignored, and how does this differ to traditional software projects? Method: We compare a corpus of 1048 Open-Source Data Science projects to a reference group of 1099 non-Data Science projects with a similar level of quality and maturity. Results: Data Science projects suffer from a significantly higher rate of functions that use an excessive numbers of parameters and local variables. Data Science projects also follow different variable naming conventions to non-Data Science projects. Conclusions: The differences indicate that Data Science codebases are distinct from traditional software codebases and do not follow traditional software engineering conventions. Our conjecture is that this may be because traditional software engineering conventions are inappropriate in the context of Data Science projects.
Code Review plays a crucial role in software quality, by allowing reviewers to discuss and critique any new patches before they can be successfully integrated into the project code. Yet, it is unsure the extent to which coding pattern changes (i.e., repetitive code) from when a patch is first submitted and when the decision is made (i.e., during the review process). In this study, we revisit coding patterns in code reviews, aiming to analyze whether or not the coding pattern changes during the review process. Comparing prior submitted patches, we measure differences in coding pattern between p…
We propose considering assurance as a model management enterprise: saying that a system is safe amounts to specifying three workflows modelling how the safety engineering process is defined and executed, and checking their conformance. These workflows are based on precise data modelling as in functional block diagrams, but their distinctive feature is the presence of relationships between the output data of a process and its input data; hence, the name ``WorkflowPlus'', WF+ . A typical WP^+ model comprises three layers: (i) process and control flow, (ii) dataflow (with input-output relationshi…
Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the basis for innovative data analysis approaches. Process mining has the strong relationship between with data mining so that it enables the bond between business intelligence approach and business process management. It focuses on end to end processes and is possible because of the growing availability of event data and new process discovery and conformance checki…
Modern code review is a common and essential practice employed in both industrial and open-source projects to improve software quality, share knowledge, and ensure conformance with coding standards. During code review, developers may inspect and discuss various changes including refactoring activities before merging code changes in the codebase. To date, code review has been extensively studied to explore its general challenges, best practices and outcomes, and socio-technical aspects. However, little is known about how refactoring activities are being reviewed, perceived, and practiced. This …
Can Language Models Go Beyond Coding? Assessing the Capability of Language Models to Build Real-World Systems
Nankai University（南开大学）；Haihe Laboratory of Information Technology Application Innovation（海河信息技术应用创新实验室）；Key Laboratory of Data and Intelligent System Security, Ministry of Education（教育部数据与智能系统安全重点实验室）；Tianjin Key Laboratory of Software Experience and Human Computer Interaction（天津市软件体验与人机交互重点实验室）；Tsinghua University（清华大学）
"Always Nice and Confident, Sometimes Wrong": Developer's Experiences Engaging Large Language Models (LLMs) Versus Human-Powered Q&A Platforms for Coding Support
Automated source code summarization is a popular software engineering research topic wherein machine translation models are employed to "translate" code snippets into relevant natural language descriptions. Most evaluations of such models are conducted using automatic reference-based metrics. However, given the relatively large semantic gap between programming languages and natural language, we argue that this line of research would benefit from a qualitative investigation into the various error modes of current state-of-the-art models. Therefore, in this work, we perform both a quantitative a…
