HeatMetro: A 3D Code Visualization for Test Debt Analysis in Evolving Systems
Resumo
Traditional test coverage metrics often fail to reveal test debt. Additionally, it is difficult to manually uncover code critical for testing, particularly in complex and rapidly changing components of large systems. However, few tools help developers visually explore test debt. In this work, we introduce HeatMetro, a web-based implementation of the code city metaphor that creates an interactive 3D software visualization and operationalizes a context-aware code coverage criterion for the visual analysis of test debt in large Java and Go projects. The feasibility of HeatMetro is evaluated through a study with developers and projects, which indicates promising results, confirming the tool’s effectiveness in identifying and explaining critical components for testing.Referências
Ahmad, A., Leifler, O., and Sandahl, K. (2022). Data visualisation in continuous integration and delivery: Information needs, challenges, and recommendations. IET software, 16(3):331–349.
Amusuo, P. C., Patil, P. V., Cochell, O., Le Lievre, T., and Davis, J. C. (2025). A unit proofing framework for code-level verification: A research agenda. In 2025 IEEE/ACM 47th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), pages 36–40. IEEE.
Aragão, B. S., Andrade, R. M., Santos, I. S., Castro, R. N., Lelli, V., and Darin, T. G. (2022). Testdcat 3.0: catalog of test debt subtypes and management activities. Software Quality Journal, 30(1):181–225.
Avelino, G., Passos, L., Hora, A., and Valente, M. T. (2016). A novel approach for estimating truck factors. In 2016 IEEE 24th International Conference on Program Comprehension (ICPC), pages 1–10. IEEE.
Brandt, C. and Ramírez, A. (2025). Towards refined code coverage: A new predictive problem in software testing. In 2025 IEEE Conference on Software Testing, Verification and Validation (ICST), pages 613–617. IEEE.
Brito, R., Brito, A., Brito, G., and Valente, M. T. (2019). Gocity: code city for go. In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 649–653. IEEE.
Chotisarn, N., Merino, L., Zheng, X., Lonapalawong, S., Zhang, T., Xu, M., and Chen, W. (2020). A systematic literature review of modern software visualization. Journal of Visualization, 23(4):539–558.
Dreef, K., Palepu, V. K., and Jones, J. A. (2023). Exploring granular test coverage and its evolution with matrix visualizations. Information and Software Technology, 155:107085.
Hasselbring, W., Krause, A., and Zirkelbach, C. (2020). Explorviz: Research on software visualization, comprehension and collaboration. Software Impacts, 6:100034.
Hernandes, V., Carvalho, A., Santos, E., Soares, Y., Oliveira, H., Barros, A., Soares, R., Lima, A., Ferreira, R., Martins, G., et al. (2025). A method for regression testing plan ordering for non-automated executions in black box testing. In Congresso Ibero-Americano em Engenharia de Software (CIbSE), pages 120–134. SBC.
Hilton, M., Bell, J., and Marinov, D. (2018). A large-scale study of test coverage evolution. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pages 53–63.
Højelse, K., Kilbak, T., Røssum, J., Jäpelt, E., Merino, L., and Lungu, M. (2022). Git-truck: Hierarchy-oriented visualization of git repository evolution. In 2022 Working Conference on Software Visualization (VISSOFT), pages 131–140. IEEE.
Ivankovic, M., Petrovic, G., Kulizhskaya, Y., Lewko, M., Kalinovcic, L., Just, R., and Fraser, G. (2024). Productive coverage: Improving the actionability of code coverage. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice, pages 58–68.
Krasner, H. (2021). The cost of poor software quality in the us: A 2020 report. Proc. Consortium Inf. Softw. QualityTM (CISQTM), 2:3.
McCabe, T. J. and Watson, A. H. (1996). Structured testing: A testing methodology. NIST Special Publication, 500:235.
Miranda, C., Avelino, G., and Santos Neto, P. (2025). Test co-evolution in software projects: A large-scale empirical study. Journal of Software: Evolution and Process, 37(7):e70035.
Mortara, J., Collet, P., and Dery-Pinna, A.-M. (2024). Visualization of object-oriented software in a city metaphor: Comprehending the implemented variability and its technical debt. Journal of Systems and Software, 208:111876.
Ostrand, T. J., Weyuker, E. J., and Bell, R. M. (2004). Where the bugs are. ACM SIGSOFT software engineering notes, 29(4):86–96.
Romano, S., Capece, N., Erra, U., Scanniello, G., and Lanza, M. (2019). The city metaphor in software visualization: feelings, emotions, and thinking. Multimedia Tools and Applications, 78(23):33113–33149.
Shatnawi, R. (2010). A quantitative investigation of the acceptable risk levels of object-oriented metrics in open-source systems. IEEE Transactions on software engineering, 36(2):216–225.
Strandberg, P. E. (2017). Software test data visualization with heatmaps–an initial survey. Report, no. MDH-MRTC318/2017-1-SE.
Strandberg, P. E., Afzal, W., and Sundmark, D. (2022). Software test results exploration and visualization with continuous integration and nightly testing. International Journal on Software Tools for Technology Transfer, 24(2):261–285.
Vasa, R., Lumpe, M., Branch, P., and Nierstrasz, O. (2009). Comparative analysis of evolving software systems using the gini coefficient. In 2009 IEEE international conference on software maintenance, pages 179–188. IEEE.
Viana, M., Moraes, E., Barbosa, G., Hora, A., and Valente, M. T. (2015). Jscity–visualização de sistemas javascript em 3d. In III Workshop de Visualização, Evolução e Manutenção de Software (VEM), pages 73–80.
Walkinshaw, N. and Minku, L. (2018). Are 20% of files responsible for 80% of defects? In Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pages 1–10.
Weichbroth, P. and Giedrowicz, M. (2024). Sus-lib: An automated tool for usability evaluation based on the software usability scale from user feedback. arXiv preprint arXiv:2410.09534.
Wettel, R. and Lanza, M. (2008). Codecity: 3d visualization of large-scale software. In Companion of the 30th international conference on Software engineering, pages 921–922.
Amusuo, P. C., Patil, P. V., Cochell, O., Le Lievre, T., and Davis, J. C. (2025). A unit proofing framework for code-level verification: A research agenda. In 2025 IEEE/ACM 47th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), pages 36–40. IEEE.
Aragão, B. S., Andrade, R. M., Santos, I. S., Castro, R. N., Lelli, V., and Darin, T. G. (2022). Testdcat 3.0: catalog of test debt subtypes and management activities. Software Quality Journal, 30(1):181–225.
Avelino, G., Passos, L., Hora, A., and Valente, M. T. (2016). A novel approach for estimating truck factors. In 2016 IEEE 24th International Conference on Program Comprehension (ICPC), pages 1–10. IEEE.
Brandt, C. and Ramírez, A. (2025). Towards refined code coverage: A new predictive problem in software testing. In 2025 IEEE Conference on Software Testing, Verification and Validation (ICST), pages 613–617. IEEE.
Brito, R., Brito, A., Brito, G., and Valente, M. T. (2019). Gocity: code city for go. In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 649–653. IEEE.
Chotisarn, N., Merino, L., Zheng, X., Lonapalawong, S., Zhang, T., Xu, M., and Chen, W. (2020). A systematic literature review of modern software visualization. Journal of Visualization, 23(4):539–558.
Dreef, K., Palepu, V. K., and Jones, J. A. (2023). Exploring granular test coverage and its evolution with matrix visualizations. Information and Software Technology, 155:107085.
Hasselbring, W., Krause, A., and Zirkelbach, C. (2020). Explorviz: Research on software visualization, comprehension and collaboration. Software Impacts, 6:100034.
Hernandes, V., Carvalho, A., Santos, E., Soares, Y., Oliveira, H., Barros, A., Soares, R., Lima, A., Ferreira, R., Martins, G., et al. (2025). A method for regression testing plan ordering for non-automated executions in black box testing. In Congresso Ibero-Americano em Engenharia de Software (CIbSE), pages 120–134. SBC.
Hilton, M., Bell, J., and Marinov, D. (2018). A large-scale study of test coverage evolution. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pages 53–63.
Højelse, K., Kilbak, T., Røssum, J., Jäpelt, E., Merino, L., and Lungu, M. (2022). Git-truck: Hierarchy-oriented visualization of git repository evolution. In 2022 Working Conference on Software Visualization (VISSOFT), pages 131–140. IEEE.
Ivankovic, M., Petrovic, G., Kulizhskaya, Y., Lewko, M., Kalinovcic, L., Just, R., and Fraser, G. (2024). Productive coverage: Improving the actionability of code coverage. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice, pages 58–68.
Krasner, H. (2021). The cost of poor software quality in the us: A 2020 report. Proc. Consortium Inf. Softw. QualityTM (CISQTM), 2:3.
McCabe, T. J. and Watson, A. H. (1996). Structured testing: A testing methodology. NIST Special Publication, 500:235.
Miranda, C., Avelino, G., and Santos Neto, P. (2025). Test co-evolution in software projects: A large-scale empirical study. Journal of Software: Evolution and Process, 37(7):e70035.
Mortara, J., Collet, P., and Dery-Pinna, A.-M. (2024). Visualization of object-oriented software in a city metaphor: Comprehending the implemented variability and its technical debt. Journal of Systems and Software, 208:111876.
Ostrand, T. J., Weyuker, E. J., and Bell, R. M. (2004). Where the bugs are. ACM SIGSOFT software engineering notes, 29(4):86–96.
Romano, S., Capece, N., Erra, U., Scanniello, G., and Lanza, M. (2019). The city metaphor in software visualization: feelings, emotions, and thinking. Multimedia Tools and Applications, 78(23):33113–33149.
Shatnawi, R. (2010). A quantitative investigation of the acceptable risk levels of object-oriented metrics in open-source systems. IEEE Transactions on software engineering, 36(2):216–225.
Strandberg, P. E. (2017). Software test data visualization with heatmaps–an initial survey. Report, no. MDH-MRTC318/2017-1-SE.
Strandberg, P. E., Afzal, W., and Sundmark, D. (2022). Software test results exploration and visualization with continuous integration and nightly testing. International Journal on Software Tools for Technology Transfer, 24(2):261–285.
Vasa, R., Lumpe, M., Branch, P., and Nierstrasz, O. (2009). Comparative analysis of evolving software systems using the gini coefficient. In 2009 IEEE international conference on software maintenance, pages 179–188. IEEE.
Viana, M., Moraes, E., Barbosa, G., Hora, A., and Valente, M. T. (2015). Jscity–visualização de sistemas javascript em 3d. In III Workshop de Visualização, Evolução e Manutenção de Software (VEM), pages 73–80.
Walkinshaw, N. and Minku, L. (2018). Are 20% of files responsible for 80% of defects? In Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pages 1–10.
Weichbroth, P. and Giedrowicz, M. (2024). Sus-lib: An automated tool for usability evaluation based on the software usability scale from user feedback. arXiv preprint arXiv:2410.09534.
Wettel, R. and Lanza, M. (2008). Codecity: 3d visualization of large-scale software. In Companion of the 30th international conference on Software engineering, pages 921–922.
Publicado
11/05/2026
Como Citar
CASILLA, Braulio Nayap Maldonado; MOGOLLON, Sergio; PARIZACA-MOZO, Paul; SARMIENTO-CALISAYA, Edgar.
HeatMetro: A 3D Code Visualization for Test Debt Analysis in Evolving Systems. In: CONGRESSO IBERO-AMERICANO EM ENGENHARIA DE SOFTWARE (CIBSE), 29. , 2026, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 106-120.
