Software documentation is key for producing high-quality projects and ensuring their smooth evolution. Nonetheless, the activity of writing software artifacts is time-consuming and effort-prone. Looking at the existing body of knowledge, we outline limited evidence of how automated approaches may support practitioners when documenting the artifacts produced throughout the software lifecycle. In particular, there is still a lack of investigations into the capabilities of Large Language Models (LLMs), which are indeed supposed to be highly beneficial in this respect. In this paper, we propose a preliminary case study to understand how LLMs can support the development of the documentation of projects developed through a Waterfall lifecycle. Using ChatGPT, we engineered specific prompts to generate and validate the artifacts produced, taking an existing, documented software engineering project as an oracle. The main findings of the study show the ability of ChatGPT to produce most artifacts correctly. In addition, we find that software engineers would require a relatively low effort to adapt the outputs provided by ChatGPT to their own context, especially for textual artifacts.

Using Large Language Models to Support Software Engineering Documentation in Waterfall Life Cycles: Are We There Yet?

Della Porta A.;De Martino V.;Recupito G.;Iemmino C.;Catolino G.;Di Nucci D.;Palomba F.
2024-01-01

Abstract

Software documentation is key for producing high-quality projects and ensuring their smooth evolution. Nonetheless, the activity of writing software artifacts is time-consuming and effort-prone. Looking at the existing body of knowledge, we outline limited evidence of how automated approaches may support practitioners when documenting the artifacts produced throughout the software lifecycle. In particular, there is still a lack of investigations into the capabilities of Large Language Models (LLMs), which are indeed supposed to be highly beneficial in this respect. In this paper, we propose a preliminary case study to understand how LLMs can support the development of the documentation of projects developed through a Waterfall lifecycle. Using ChatGPT, we engineered specific prompts to generate and validate the artifacts produced, taking an existing, documented software engineering project as an oracle. The main findings of the study show the ability of ChatGPT to produce most artifacts correctly. In addition, we find that software engineers would require a relatively low effort to adapt the outputs provided by ChatGPT to their own context, especially for textual artifacts.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4893537
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact