Qualitative and Quantitative Evaluation of Static Code Analysis Tools

poster abstractStatic code analysis (SCA) is a methodology of detecting errors in programs without actually compiling the source code to binary format and executing it on a machine. The main goal of a SCA tool is to aid developers in quickly identifying errors that can jeopardize the security and integrity of the program. With the vast array of SCA tools available, each specializing in particular languages, error types, and detection methodologies, choosing the optimal tool(s) can be a daunting task for any software developer, or organization. This, however, is not a problem associated only with SCA tools, but applies to any application domain where many tools exist and a selection of a subset of these tools is needed for effectively tackling a given problem. To address this fundamental challenge with selecting the most appropriate SCA tool for a particular problem, this research is performing a comprehensive study of different available SCA tool, both commercial and open-source. The end goal of this study is to not only evaluate how different SCA tools perform with respect to locating specific errors in source code (i.e., the quality of the tool), but to model the behavior of each SCA tool using quantitative metrics gathered from the source code, such as source lines of code (SLOC), cyclometic complexity, and function points. The behavioral model can then be used to prescreen existing (and new) source code, and select the most appropriate SCA tool, or set of SCA tools, that can identify the most errors in the source code undergoing analysis

MRI Findings in People with Epilepsy and Nodding Syndrome in an Area Endemic for Onchocerciasis: An Observational Study.

Onchocerciasis has been implicated in the pathogenesis of epilepsy. The debate on a potential causal relationship between Onchocerca volvulus and epilepsy has taken a new direction in the light of the most recent epidemic of nodding syndrome. To document MRI changes in people with different types of epilepsy and investigate whether there is an association with O. volvulus infection. In a prospective study in southern Tanzania, an area endemic for O. volvulus with a high prevalence of epilepsy and nodding syndrome, we performed MRI on 32 people with epilepsy, 12 of which suffered from nodding syndrome. Polymerase chain reaction (PCR) of O. volvulus was performed in skin and CSF. The most frequent abnormalities seen on MRI was atrophy (twelve patients (37.5%)) followed by intraparenchymal pathologies such as changes in the hippocampus (nine patients (28.1%)), gliotic lesions (six patients (18.8%)) and subcortical signal abnormalities (three patients (9.4%)). There was an overall trend towards an association of intraparenchymal cerebral pathologies and infection with O. volvulus based on skin PCR (Fisher's Exact Test p=0.067) which was most pronounced in children and adolescents with nodding syndrome compared to those with other types of epilepsy (Fisher's Exact Test, p=0.083). Contrary to skin PCR results, PCR of CSF was negative in all patients. The observed trend towards an association of intraparenchymal cerebral pathological results on MRI and a positive skin PCR for O. volvulus despite negative PCR of CSF is intriguing and deserves further attention