Ten years of maintenance, nine published revisions of the standards for the Testing and Test Control Notation version 3 (TTCN-3), more than 500 change requests since 2006, and 10 years of activity on the official TTCN-3 mailing list add up to a rich history, not unlike that of many successful Open Source Software (OSS) projects. In this article, we contemplate TTCN-3 in the context of software evolution and examine its history quantitatively. We mined the changes in the textual content of the standards, the data in change requests from the past 5 years, and the mailing list archives from the past 10 years. In addition, to characterize the use of the TTCN-3 we investigated the meta-data of the contributions at the TTCN-3 User Conference, and the use of language constructs in a large-scale TTCN-3 test suite. Based on these data sets, we first analyze the amount, density, and location of changes within the different parts of the standard. Then, we analyze the activity and focus of the user community and the maintenance team in both the change request management system and the official TTCN-3 mailing list. Finally, we analyze the distribution of contributions at the TTCN-3 User Conference across different topics over the past 8 years and construct use anomalies during the development of a large-scale test suite. Our findings indicate that the TTCN-3 is becoming increasingly stable as the overall change density and intensity, as well as the number of change requests are decreasing, despite the monotonous increase in the size of the standards. 相似文献
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Image post-processing corrects for cardiac and respiratory motion (MoCo) during cardiovascular magnetic resonance (CMR) stress perfusion. The study analyzed its influence on visual image evaluation.
Materials and methods
Sixty-two patients with (suspected) coronary artery disease underwent a standard CMR stress perfusion exam during free-breathing. Image post-processing was performed without (non-MoCo) and with MoCo (image intensity normalization; motion extraction with iterative non-rigid registration; motion warping with the combined displacement field). Images were evaluated regarding the perfusion pattern (perfusion deficit, dark rim artifact, uncertain signal loss, and normal perfusion), the general image quality (non-diagnostic, imperfect, good, and excellent), and the reader’s subjective confidence to assess the images (not confident, confident, very confident).
Results
Fifty-three (non-MoCo) and 52 (MoCo) myocardial segments were rated as ‘perfusion deficit’, 113 vs. 109 as ‘dark rim artifacts’, 9 vs. 7 as ‘uncertain signal loss’, and 817 vs. 824 as ‘normal’. Agreement between non-MoCo and MoCo was high with no diagnostic difference per-patient. The image quality of MoCo was rated more often as ‘good’ or ‘excellent’ (92 vs. 63%), and the diagnostic confidence more often as “very confident” (71 vs. 45%) compared to non-MoCo.
Conclusions
The comparison of perfusion images acquired during free-breathing and post-processed with and without motion correction demonstrated that both methods led to a consistent evaluation of the perfusion pattern, while the image quality and the reader’s subjective confidence to assess the images were rated more favorably for MoCo.
