Commands as AI Conversations

Developers and data scientists often struggle to write command-line inputs, even though graphical interfaces or tools like ChatGPT can assist. The solution? "ai-cli," an open-source system inspired by GitHub Copilot that converts natural language prompts into executable commands for various Linux command-line tools. By tapping into OpenAI's API, which allows interaction through JSON HTTP requests, "ai-cli" transforms user queries into actionable command-line instructions. However, integrating AI assistance across multiple command-line tools, especially in open source settings, can be complex. Historically, operating systems could mediate, but individual tool functionality and the lack of a unified approach have made centralized integration challenging. The "ai-cli" tool, by bridging this gap through dynamic loading and linking with each program's Readline library API, makes command-line interfaces smarter and more user-friendly, opening avenues for further enhancement and cross-platform applicability.Comment: 5 page

Open Source Adoption In Large US Companies

Various organizations increasingly adopt open source software, both on desktop PCs and servers. Since the first movements in open source in the 1960’s its growth has lead to new approaches in software development, licensing, and distribution, as well as in software vendors’ business models. The literature includes very interesting studies regarding prospective benefits, business models and case studies. However, the adoption of open source in large, global companies and its relationship with factors such as profitability, revenues and industry sector has not yet been researched. This study aims to answer these questions based on data we collected from Fortune 1000 companies and provides a method that can be applied in similar contexts

Definitions of a Software Smell

Many authors have defined smells from their perspective. This document attempts to provide a consolidated list of such definitions

Global software development in the freeBSD project

Freebsd is a sophisticated operating system developed and maintained as open-source software by a team of more than 350 individuals located throughout the world. This study uses developer location data, the configuration management repository, and records from the issue database to examine the extent of global development and its effect on produc-tivity, quality, and developer cooperation. The key findings are that global development allows round-the-clock work, but there are some marked differences between the type of work performed at different regions. The effects of multiple dispersed developers on the quality of code and productiv-ity are negligible. Mentoring appears to be sometimes as-sociated with developers living closer together, but ad-hoc cooperation seems to work fine across continents

Software engineering for deep learning applications: usage of SWEng and MLops tools in GitHub repositories

The rising popularity of deep learning (DL) methods and techniques has invigorated interest in the topic of SE4DL, the application of software engineering (SE) practices on deep learning software. Despite the novel engineering challenges brought on by the data-driven and non-deterministic paradigm of DL software, little work has been invested into developing AI-targeted SE tools. On the other hand, tools tackling more general engineering issues in DL are actively used and referred to under the umbrella term of ``MLOps tools''. Furthermore, the available literature supports the utility of conventional SE tooling in DL software development. Building upon previous MSR research on tool usage in open-source software works, we identify conventional and MLOps tools adopted in popular applied DL projects that use Python as the main programming language. About 70% of the GitHub repositories mined contained at least one conventional SE tool. Software configuration management tools are the most adopted, while the opposite applies to maintenance tools. Substantially fewer MLOps tools were in use, with only 9 tools out of a sample of 80 used in at least one repository. The majority of them were open-source rather than proprietary. One of these tools, TensorBoard, was found to be adopted in about half of the repositories in our study. Consequently, the use of conventional SE tooling demonstrates its relevance to DL software. Further research is recommended on the adoption of MLOps tooling by open-source projects, focusing on the relevance of particular tool types, the development of required tools, as well as ways to promote the use of already available tools

Quieting the Static: A Study of Static Analysis Alert Suppressions

Static analysis tools are commonly used to detect defects before the code is released. Previous research has focused on their overall effectiveness and their ability to detect defects. However, little is known about the usage patterns of warning suppressions: the configurations developers set up in order to prevent the appearance of specific warnings. We address this gap by analyzing how often are warning suppression features used, which warning suppression features are used and for what purpose, and also how could the use of warning suppression annotations be avoided. To answer these questions we examine 1\,425 open-source Java-based projects that utilize Findbugs or Spotbugs for warning-suppressing configurations and source code annotations. We find that although most warnings are suppressed, only a small portion of them get frequently suppressed. Contrary to expectations, false positives account for a minor proportion of suppressions. A significant number of suppressions introduce technical debt, suggesting potential disregard for code quality or a lack of appropriate guidance from the tool. Misleading suggestions and incorrect assumptions also lead to suppressions. Findings underscore the need for better communication and education related to the use of static analysis tools, improved bug pattern definitions, and better code annotation. Future research can extend these findings to other static analysis tools, and apply them to improve the effectiveness of static analysis.Comment: 11 pages, 4 figure