Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book

Advances in natural language processing have resulted in large language models (LLMs) that are capable of generating understandable and sensible written text. Recent versions of these models, such as OpenAI Codex and GPT-3, can generate code and code explanations. However, it is unclear whether and how students might engage with such explanations. In this paper, we report on our experiences generating multiple code explanation types using LLMs and integrating them into an interactive e-book on web software development. We modified the e-book to make LLM-generated code explanations accessible through buttons next to code snippets in the materials, which allowed us to track the use of the explanations as well as to ask for feedback on their utility. Three different types of explanations were available for students for each explainable code snippet; a line-by-line explanation, a list of important concepts, and a high-level summary of the code. Our preliminary results show that all varieties of explanations were viewed by students and that the majority of students perceived the code explanations as helpful to them. However, student engagement appeared to vary by code snippet complexity, explanation type, and code snippet length. Drawing on our experiences, we discuss future directions for integrating explanations generated by LLMs into existing computer science classrooms

Formal Requirements Elicitation with FRET

FRET is a tool for writing, understanding, formalizing and analyzing requirements. Users write requirements in an intuitive, restricted natural language, called FRETISH, with precise, unambiguous meaning. For a FRETISH requirement, FRET: 1) produces natural language and diagrammatic explanations of its exact meaning, 2) formalizes the requirement in logics, and 3) supports interactive simulation of produced logic formulas to ensure that they capture user intentions. FRET connects to analysis tools by facilitating the mapping between requirements and models/code, and by generating verification code. FRET is available open source at https://github.com/NASA-SW-VnV/fret; a video can be accessed at : https://tinyurl.com/fretForREFSQ

A system overview of the Aerospace Safety Research and Data Institute data management programs

The NASA Aerospace Safety Information System, is an interactive, generalized data base management system. The on-line retrieval aspects provide for operating from a variety of terminals (or in batch mode). NASIS retrieval enables the user to expand and display (review) the terms of index (cross reference) files, select desired index terms, combine sets of documents corresponding to selected terms and display the resulting records. It also allows the user to print (record) this information on a high speed printer if desired. NASIS also provides the ability to store the strategy of any given session the user has executed. It has a searching and publication ability through generalized linear search and report generating modules which may be performed interactively or in a batch mode. The user may specify formats for the terminal from which he is operating. The system features an interactive user's guide which explains the various commands available and how to use them as well as explanations for all system messages. This explain capability may be extended, without program changes, to include descriptions of the various files in use. Coupled with the ability of NASIS to run in an MTT (multi-terminal task) mode is its automatic accumulation of statistics on each user of the system as well as each file

Mechanisms in Dynamically Complex Systems

In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. I want to draw the attention to another, radically different kind of complex system, in fact one that many scientists regard as the only genuine kind of complex system. Instead of being compositionally complex these systems rather exhibit highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of non-linearly interacting constituents. The characteristic dynamical patterns in what I call “dynamically complex systems” arise from the interaction of the system’s parts largely irrespective of many properties of these parts. Dynamically complex systems can exhibit surprising statistical characteristics, the robustness of which calls for an explanation in terms of underlying generating mechanisms. However, I want to argue, dynamically complex systems are not sufficiently covered by the available conceptions of mechanisms. I will explore how the notion of a mechanism has to be modified to accommodate this case. Moreover, I will show under which conditions the widespread, if not inflationary talk about mechanisms in (dynamically) complex systems stretches the notion of mechanisms beyond its reasonable limits and is no longer legitimate