Developers have a lot of freedom in writing comments as well as in choosing identifiers and method names. These are intentional in nature and provide a different relevance of information to understand what a software system implements, and in particular the role of each source file.In this paper we investigate the effectiveness of exploiting lexical information for software system clustering. In particular we explore the contribution of the combined use of six different dictionaries, corresponding to the six parts of the source code where programmers introduce lexical information, namely: class, attribute, method and parameter names, comments, and source code statements. Their relevance has been weighted by means of a probabilistic model, whose parameters have been estimated by the Expectation-Maximization algorithm. To group source files accordingly we used a hierarchical clustering algorithm. The investigation has been conducted on a dataset of 13 open source Java software systems.

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Background Medullary thyroid carcinoma is a neuroendocrine tumor belonging form a malignant growth of the thyroid parafollicular C-cells, representing from 1 to 10% of all thyroid cancer. The biochemical activity of medullary thyroid carcinoma includes the production of calcitonin and carcinoembryogenic antigen, which are sensitive tumor markers, facilitating the diagnosis, follow-up and prognostication. The diagnosis is reached through the identification of high basal calcitonin serum level or after pentagastrin stimulation test. Medullary thyroid carcinoma is able to produce other relevant biomarkers as procalcitonin, carcinoembryionic antigen and chromogranin A. In Literature are described few cases of medullary thyroid carcinoma without elevation of serum calcitonin, an extremely rare event. The aim of this study was to analyse the presentation, the main features and therapeutic management of medullary thyroid carcinoma associated with negative serum calcitonin levels. Methods Using the PubMed database, a systematic review of the current Literature was carried out, up to February 2018. Finally, nineteen articles met our inclusion criteria and were selected according to the modified Newcastle-Ottawa scale. Results Fourty-nine patients with definitive pathology confirming medullary thyroid carcinoma and with calcitonin serum level in the normal range were identified (24 female, 24 male and not reported gender in 1 case). Mean age was 51.7 years. Serum calcitonin levels were reported for 20 patients with a mean value of 8.66 pg/mL and a range of 0.8–38 pg/mL. Despite the low or undetectable calcitonin serum level, at immunochemistry in almost the half of the cases reported by the Authors, the tumors presented diffuse or focal positivity for calcitonin and carcinoembryionic antigen, while was reported a chromogranin A positivity in 41 of the 43 tested patients. Conclusions Calcitonin negative medullary thyroid carcinoma is an extremely rare pathology. The diagnosis and the surveillance is often challenging and delayed, due to the lack of elevation of serum markers as calcitonin and carcinoembryionic antigen. Further studies are needed, to better define options for management of non secretory medullary thyroid carcinoma and to identify new and reliable biomarkers associated to diagnosis and relapse of this medical dilemma.

Abstract. In some studies, Support Vector Machines (SVMs) have been turned out to be promising for predicting fault-prone software components. Nevertheless, the performance of the method depends on the setting of some parameters. To address this issue, we propose the use of a Genetic Algorithm (GA) to search for a suitable configuration of SVMs parameters that allows us to obtain optimal prediction performance. The approach has been assessed carrying out an empirical analysis based on jEdit data from the PROMISE repository. We analyzed both the inter-and the intra-release performance of the proposed method. As benchmarks we exploited SVMs with Grid-search and several other machine learning techniques. The results show that the proposed approach let us to obtain an improvement of the performance with an increasing of the Recall measure without worsening the Precision one. This behavior was especially remarkable for the inter-release use with respect to the other prediction techniques.

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