Abstract: Although software development is an inherently human activity, research in software engineering (SE) has long focused mostly on processes and tools, failing to recall about the human factors behind. Even when explored, researchers typically do not properly use psychology background to better understand human factors in SE, such as the psychometric instruments, which aim to measure human factors. Our goal is to provide a critical review on the use of psychometric instruments in SE research regarding personality. We present a two-step study. First, a systematic mapping of the literature in order to generate a catalog of the psychometric instruments used; second, a preliminary survey to be conducted with social sciences researchers to assess their adoption in SE research. The results so far are quite initial. The next steps direct us to finish the data extraction to finalize the catalog (systematic mapping) and to refine the survey design and apply it with social sciences researchers.

[MCC04/21]
GARCIA, A.; HAEUSLER, H. E. An extension to the minimal grammar problem. September, 2021. 16 p. Eng.  E-mail: hermann@inf.puc-rio.br

Abstract: The smallest grammar problem consists of finding the smallest CFG that generates only a given string. This is an NP-hard problem, and there are various heuristics to solve it described in the literature. In this paper, we propose an extension to this problem, allowing the grammars to have a reverse operator and a complement operator. We argue that the heuristics used to find approximate solutions to the smallest grammar problem can be easily adapted to these extensions. Experiments confirm that these extensions perform better than plain CFGs when applied to virus genome compression.

[MCC05/21]
DUARTE, J. C.; COLCHER, S. Building a noisy audio dataset to evaluate machine learning approaches for automatic speech recognition systems. September, 2021. 17 p. Eng.  E-mail: colcher@inf.puc-rio.br

Abstract: Automatic speech recognition systems are part of people’s daily lives, embedded in personal assistants and mobile phones, helping as a facilitator for human-machine interaction while allowing access to information in a practically intuitive way. Such systems are usually implemented using machine learning techniques, especially with deep neural networks. Even with its high performance in the task of transcribing text from speech, few works address the issue of its recognition in noisy environments and, usually, the datasets used do not contain noisy audio examples, while only mitigating this issue using data augmentation techniques. This work aims to present the process of building a dataset of noisy audios, in a specific case of degenerated audios due to interference, commonly present in radio transmissions. Additionally, we present initial results of a classifier that uses such data for evaluation, indicating the benefits of using this dataset in the recognizer’s training process. Such recognizer achieves an average result of 0.4116 in terms of character error rate in the noisy set (SNR = 30).

[MCC06/21]
BORGES, L.; PEREIRA, V.; MAGALHÃES, P.; LIMA, F.; ENDLER, M. Estudo de algoritmos distribuídos na prática: exemplos e comparações. November, 2021. 32 p. Por.  E-mail: endler@inf.puc-rio.br

Abstract: This text presents examples of the use of distributed algorithms in several application areas - to solve very specific problems of coordination and communication between distributed instances - followed by a discussion of the advantages and limitations of the solutions. We hope that this tech report will contribute to a broader understanding of interesting distributed problems and the proposed solutions, as well as to a better understanding of the possible applications. And the relevance and actuality of these algorithms in the realization of global computational systems, increasingly connected and complex.