2020
Autores
Vinagre, J; Jorge, AM; Ghossein, MA; Bifet, A;
Publicação
ORSUM@RecSys
Abstract
2020
Autores
Oliveira, A; Freitas, R; Jorge, A; Amorim, V; Moniz, N; Paiva, ACR; Azevedo, PJ;
Publicação
Intelligent Data Engineering and Automated Learning - IDEAL 2020 - 21st International Conference, Guimaraes, Portugal, November 4-6, 2020, Proceedings, Part II
Abstract
In today’s software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and effort. This paper presents a framework that generates software test cases automatically based on user interaction data. We propose a data-driven software test generation solution that combines the use of frequent sequence mining and Markov chain modeling. We assess the quality of the generated test cases by empirically evaluating their coverage with respect to observed user interactions and code. We also measure the plausibility of the distribution of the events in the generated test sets using the Kullback-Leibler divergence. © 2020, Springer Nature Switzerland AG.
2020
Autores
Jorge, AM; Campos, R; Jatowt, A; Aizawa, A;
Publicação
CEUR Workshop Proceedings
Abstract
2020
Autores
Jorge, AM; Campos, R; Jatowt, A; Aizawa, A;
Publicação
AI4Narratives@IJCAI
Abstract
2020
Autores
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S; Pasquali, A; Cordeiro, JP; Rocha, C; Mansouri, B; Santana, BS;
Publicação
SIGIR Forum
Abstract
2020
Autores
Nunes, S; Little, S; Bhatia, S; Boratto, L; Cabanac, G; Campos, R; Couto, FM; Faralli, S; Frommholz, I; Jatowt, A; Jorge, A; Marras, M; Mayr, P; Stilo, G;
Publicação
SIGIR Forum
Abstract
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