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Impacto da latência na verificação e do atraso acentuado na rotulação para detecção de falhas de software
(2023-04-25) Sgotti, Lucas Cordeiro; Cabral, George Gomes; http://lattes.cnpq.br/8227256452129177; http://lattes.cnpq.br/6243434551046357
Software Defect Prediction is an activity inherent to software development and it requires a high amount of human effort. This activity is often not prioritized in order toreduce the project's expenses. Just-in-Time Software Defect Prediction (JIT-SDP) is one of the approaches used for predicting software defects in order to automatically identify, through machine learning methods, software artifacts likely to contain defects based on historical data. However, most approaches assume that the characteristics of the problem remain the same over time, but the imbalance between classes is a problem that evolves over time as new training examples arrive, for example. Analyzing the problem in an online environment means that, among other things, there is an intrinsic chronological aspect to be considered which, in turn, brings with it some issues, among them verification latency, which refers to the fact that training example labels can arrive much later than their characteristics. This work aims to investigate the impact of verification latency on the problem of detecting defects in software, as well as the performance of these methods according to the degree of verification latency in examples of the defect inducing class.
Previsão de resultados de jogos do campeonato brasileiro de futebol utilizando aprendizagem de máquina
(2019-02-05) Almeida, Luiz Alberes Bispo de; Carvalho, Tiago Buarque Assunção de; http://lattes.cnpq.br/7150833804013500
Over the past few years, the demand for sports betting has grown, and several people started living oﬀ this market. Using Machine Learning with the goal of making game analysis for betting more comfortable as well as measuring proﬁts, a database for the Brazilian Soccer Championship Série A 2017 was created, encompassing features from two opposing teams and the match. For testing in an evaluation model, the Naive Bayes technique was chosen, simulating a Brazilian Championship round, which contains ten matches. The evaluation model was executed both with and without correct probability restrictions, with the goal of reducing errors. Three scenarios were used on the evaluation model, the ﬁrst having two classes that consider goals by both teams, the second regarding the total match goals (over or below 2.5 goals) and the third considering all three possible match results (a victory for one side, a tie, or a victory for the other side). The results without probability restriction reached its higher value at 5.51% of average and total proﬁt. However, regarding the results with probability restriction, the best average gain was 36.05%, and the best total proﬁt was 39.13%, both using 99% correct probability restriction.