Influência da revisão de atividades executadas para melhoria da acurácia na estimativa de software utilizando planning poker

Abstract – Background – The software effort estimation research area aims to improve the accuracy of this estimation in software projects and activities. Aims – This study describes the development and usage of a web application tocollect data generated from the Planning Poker estimation process and the analysis of the collected data to investigate the impact of revising previous estimates when conducting similar estimates in a Planning Poker context. Method – Software activities were estimated by Universidade Tecnológica Federal do Paraná (UTFPR) computer students, using Planning Poker, with and without revising previous similar activities, storing data regarding the decision-making process. And the collected data was used to investigate the impact that revising similar executed activities have in the software effort estimates' accuracy.Obtained Results – The UTFPR computer students were divided into 14 groups. Eight of them showed accuracy increase in more than half of their estimates. Three of them had almost the same accuracy in more than half of their estimates. And only three of them had loss of accuracy in more than half of their estimates. Conclusion – Reviewing the similar executed software activities, when using Planning Poker, led to more accurate software estimates in most cases, and, because of that, can improve the software development process.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.