Miguel A. Sicilia

Empirical findings on team size and productivity in software development

The size of software project teams has been considered to be a driver of project productivity. Although there is a large literature on this, new publicly available software repositories allow us to empirically perform further research. In this paper we analyse the relationships between productivity, team size and other project variables using the International Software Benchmarking Standards Group (ISBSG) repository. To do so, we apply statistical approaches to a preprocessed subset of the ISBSG repository to facilitate the study. The results show some expected correlations between productivity, effort and time as well as corroborating some other beliefs concerning team size and productivity. In addition, this study concludes that in order to apply statistical or data mining techniques to these type of repositories extensive preprocessing of the data needs to be performed due to ambiguities, wrongly recorded values, missing values, unbalanced datasets, etc. Such preprocessing is a difficult and error prone activity that would need further guidance and information that is not always provided in the repository.

Comparing Bayesian inference and case-based reasoning as support techniques in the diagnosis of Acute Bacterial Meningitis

The amount of information available for physicians has dramatically increased in the recent past. In contrast, the specialists ability to understand, synthesize and take into account such information is severely constrained by the short time available for the appointments. Therefore, systems reusing available knowledge and implementing reasoning processes become critical to support the tasks of the doctors. As a number of different techniques for building such systems are available, contrasting their effectiveness becomes a major concern. This is especially important in the case of infectious diseases that can be lethal within hours such as the Acute Bacterial Meningitis (ABM) for which implementing and contrasting different techniques allows for an increased reliability and speed in supporting the process of diagnosis. This work focuses on the construction of diagnosis support tools for ABM, reporting a comparative assessment of the quality of a Clinical Decision Support System (CDSS) resulting from the application of Case Based Reasoning (CBR), to that of an existing CDSS system developed using a Bayesian expert system. Although both approaches proved to be useful, the one based in CBR techniques show some interesting capabilities as higher precision, automatic learning or experience capturing, and also a better response to lack of input data. The three developed systems perform with high levels of accuracy- e.g. propose correct diagnostics based on a certain set of symptoms - but the one based on CBR present some additional capabilities that look very promising for implementing these kind of systems in a real world scenario.

Segmented parametric software estimation models: using the EM algorithm with the ISBSG 8 database

Parametric software estimation mod- els rely on the availability of historical project databases from which estimation models are de- rived. In the case of large project databases with data coming from heterogeneous sources, a sin- gle mathematical model cannot properly capture the diverse nature of the projects under consider- ation. In this paper, a clustering algorithm have been used as a tool to produce segmented models. A concrete case study using a modified EM algo- rithm is reported. In cases in which the cluster- ing algorithm produces an statistical characteri- zation of each of the resulting clusters, as happens with the EM algorithm, such representations can be used as similarity metrics to derive analogical estimates. This can be put in contrast with de- riving partial parametric models for each cluster. The paper also provides a comparison of quality of adjustment of both approaches.

Segmentation of software engineering datasets using the m5 algorithm

This paper reports an empirical study that uses clustering techniques to derive segmented models from software engineering repositories, focusing on the improvement of the accuracy of estimates. In particular, we used two datasets obtained from the International Software Benchmarking Standards Group (ISBSG) repository and created clusters using the M5 algorithm. Each cluster is associated with a linear model. We then compare the accuracy of the estimates so generated with the classical multivariate linear regression and least median squares. Results show that there is an improvement in the accuracy of the results when using clustering. Furthermore, these techniques can help us to understand the datasets better; such techniques provide some advantages to project managers while keeping the estimation process within reasonable complexity.

Information economy philosophy in universal education. The Open Educational Resources (OER): Technical, socioeconomics and legal aspects

According to Dr. B.R. Ambedkarpsilas definition by Deshpande, P.M. (1995), Open Educational Resources (OER) are based on the philosophical view of knowledge as a collective, social product. In consequence, it is also desirable to make it a social property. Terry Foote, one of the Wikipedia projectpsilas chairperson emphasize this: ldquoImagine a world in which every single person is given free access to the sum of all human knowledgeldquo . The importance of open educational resources (OERs) has been widely documented and demonstrated and a high magnitude impact is to be expected for OERs in the near future. This paper presents on overview of OERs and its current usage. Then, the paper goes into detailed some related aspects. Which is the impact, in socio-economic terms, of OER, especially for the less developed? Which legal aspects influence the diffusion and use of OER? And, which are the technical resources needed for them?.