A SonarQube Static Analysis of the Spectral Workbench

A SonarQube Static Analysis of the Spectral Workbench

Ifeanyi Rowland Onyenweaku1*, Michael Scott Brown2, Michael Pelosi3, M. H. Shahine1

1Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, United States.
2University of Maryland Global Campus, Adelphi, Maryland, United States.
3Texas A&M Texarkana, Texarkana, Texas, United States.

The Spectral Workbench is an open-source, community driven software suite to obtain and disseminate spectral data. It consists of a client application that collects spectral readings and a server application that is an online database of spectral data. It is difficult to detect software defects in the Spectral Workbench application. A static analysis tool, SonarQube, was selected to find these defects. Numerous defects were detected and documented. SonarQube will increase the reliability of the Spectral Workbench, which provides numerous benefits including increased confidence in its data and effectiveness which will drive additional number of users for spectral repository data collection.

Keywords: SonarQube; Static analysis; Defects; Security; Spectral Workbench

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How to cite this article:
Ifeanyi Rowland Onyenweaku, Michael Scott Brown, Michael Pelosi, M. H. Shahine. A SonarQube Static Analysis of the Spectral Workbench. International Journal of Natural Science and Reviews, 2021; 6:16. DOI: 10.28933/ijnsr-2020-12-0605


1. https://spectralworkbench.org/
2. Ammann, P., & Offutt, J. (2016). Introduction to software testing. Cambridge University Press.
3. Desai, S., & Srivastava, A. (2016). Software testing: a practical approach. PHI Learning Pvt. Ltd.
4. https://www.sonarqube.org/
5. Balado Sánchez, C., Díaz Redondo, R. P., Fer-nández Vilas, A., & Sánchez Bermúdez, A. M. (2019). Spectrophotometers for labs: A cost‐efficient solution based on smartphones. Computer Applications in Engi-neering Education, 27(2), 371-379.
6. Mayerhöfer, T. G., & Popp, J. (2019). Beer’s Law–Why Absorbance Depends (Almost) Linearly on Concentration. ChemPhysChem, 20(4), 511-515.
7. Padalia, H., Pandey, K., & Arumugam, R. A. (2017). Development of a web–enabled spectral data archival, visualisation and analysis archi-tecture for tropical phytodiversity invento-ry. Tropical Ecology, 58(2), pp. 307-314.
8. Ayewah, N., Pugh, W., Morgenthaler, J. D., Penix, J., & Zhou, Y. (2007, June). Evaluating static analysis defect warnings on production software. In Proceedings of the 7th ACM SIG-PLAN-SIGSOFT workshop on Program analysis for software tools and engineering, pp. 1-8.
9. Staiger, S. (2007, March). Static analysis of pro-grams with graphical user interface. In 11th Eu-ropean Conference on Software Maintenance and Reengineering, pp. 252-264. IEEE.
10. Adzemovic, H. (2020). A template-based approach to automatic program repair of Sonarqube static warnings. (Master’s thesis).
11. García-Munoz, J., García-Valls, M., & Escrib-ano-Barreno, J. (2016). Improved metrics handling in SonarQube for software quality monitoring. In Distributed Computing and Artificial Intelligence, 13th International Conference, pp. 463-470. Springer, Cham.
12. Guaman, D., Sarmiento, P.A., Barba-Guaman, L., Cabrera, P. and Enciso, L. (2017). SonarQube as a Tool to Identify Software Metrics and Technical Debt in the Source Code through Static Analysis, Proceedings of 2017 the 7th International Work-shop on Computer Science and Engineering, Bei-jing, pp 171-175.