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Publicações

Publicações por Jorge Melegati

2023

Experimentation in Early-Stage Video Game Startups: Practices and Challenges

Autores
Edison, H; Melegati, J; Bjarnason, E;

Publicação
ICSOB

Abstract
AbstractExperimentation has been considered critical for successful software product and business development, including in video game startups. Video game startups need “wow” qualities that distinguish them from the competition. Thus, they need to continuously experiment to find these qualities before running out of time and resources. In this study, we aimed to explore how these companies perform experimentation. We interviewed four co-founders of video game startups. Our findings identify six practices, or scenarios, through which video game startups conduct experiments and challenges associated with these. The initial results could inform these startups about the possibilities and challenges and guide future research.

2023

Anti-patterns in managing uncertain Non-Functional Requirements

Autores
Viviani, L; Guerra, E; Melegati, J; Daniel, J;

Publicação
EuroPLoP

Abstract

2023

CADV: A software visualization approach for code annotations distribution

Autores
Lima, P; Melegati, J; Gomes, E; Pereira, NS; Guerra, E; Meirelles, P;

Publicação
Inf. Softw. Technol.

Abstract

2023

Impact in Software Engineering Activities After One Year of COVID-19 Restrictions for Startups and Established Companies

Autores
Hooshyar, H; Guerra, E; Melegati, J; Khanna, D; Aldaeej, A; Matturro, G; Zaina, LAM; Greer, D; Rafiq, U; Chanin, R; Wang, X; Garbajosa, J; Abrahamsson, P; Khomh, F; Duc, AN;

Publicação
IEEE Access

Abstract

2024

Some things never change: how far generative AI can really change software engineering practice

Autores
Campos, Ad; Melegati, J; Nascimento, N; Chanin, R; Sales, A; Wiese, I;

Publicação
CoRR

Abstract

2024

Generative AI for Test Driven Development: Preliminary Results

Autores
Mock, M; Melegati, J; Russo, B;

Publicação
XP Workshops

Abstract
AbstractTest Driven Development (TDD) is one of the major practices of Extreme Programming for which incremental testing and refactoring trigger the code development. TDD has limited adoption in the industry, as it requires more code to be developed and experienced developers. Generative AI (GenAI) may reduce the extra effort imposed by TDD. In this work, we introduce an approach to automatize TDD by embracing GenAI either in a collaborative interaction pattern in which developers create tests and supervise the AI generation during each iteration or a fully-automated pattern in which developers only supervise the AI generation at the end of the iterations. We run an exploratory experiment with ChatGPT in which the interaction patterns are compared with the non-AI TDD regarding test and code quality and development speed. Overall, we found that, for our experiment and settings, GenAI can be efficiently used in TDD, but it requires supervision of the quality of the produced code. In some cases, it can even mislead non-expert developers and propose solutions just for the sake of the query.

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