Application of Computer Vision to image review process in the software development

  • Wendell Marques Sidia Institute of Science and Technology
  • Andrezza De Melo Bonfim Sidia Institute of Science and Technology
  • Eduardo Filho Sidia Institute of Science and Technology
  • Edluce Leitao Veras Sidia Institute of Science and Technology
  • Daniel Souza Sidia Institute of Science and Technology
  • Vinicius Gabriel da Silva Sidia Institute of Science and Technology

Abstract


The main goal of reviews in software development is to grant the quality and correctness of the products. One of those tasks that we can highlight is image review. This process could require more time to process and tends to be less accurate due to human error. Considering that developers who perform this type of task also have other responsibilities, the longer the review takes, the more work time is required, and this can affect the time of deliverables in the industry. Therefore, the proposal of this research is to use Optical Character Recognition (OCR) and Computer Vision (CV), to automate the process of image review, for the interpretation and validation of the content, both textual and objects. To carry out this research a set of 100 images was used in manual and automated review process and was verified that the automated approach reduced the execution time by 96.45%, and reduced the number of errors by 4%, proving that it is feasible in this development context. The application of this tool helped to increase efficiency by reducing the time spent on manual reviews, increased the consistency of image analysis, ensuring better accuracy compared to manual reviews. It also promoted improvements in development and the team, through a more productive work environment.
Keywords: Computer Vision, reviews in software development, software engineering, systems for automation, Optical character recognition

References

Cheryl Angelica, Hendrik Purnama, Fredy Purnomo, et al. 2021. Impact of computer vision with deep learning approach in medical imaging diagnosis. In 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI), Vol. 1. IEEE, ACM Press, New York, NY, 37–41.

T Arrighi, JE Rojas, JC Soto, CA Madrigal, and JA Londono. 2012. Recognition and classification of numerical labels using digital image processing techniques. In 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA). IEEE, 252–260.

Erika Ap Garefa dos Santos, Gilmar Barros Ferreira, and Mariela Ferreira. 2023. AGRICULTURA 4.0: estudo de caso sobre a eficiência da indústria 4.0 aplicada ao agronegócio. Ciência & Tecnologia 15, 1 (2023), e1517–e1517.

Kenish Rajesh Halani, Kavita, and Rahul Saxena. 2021. Critical Analysis of Manual Versus Automation Testing. In 2021 International Conference on Computational Performance Evaluation (ComPE). 132–135. DOI: 10.1109/ComPE53109.2021.9752388

S. Rama Krishna. 2025. Text Detection and Extraction Using OpenCV and OCR. International Scientific Journal of Engineering and Management (March 2025).

CH Nanda Kumar, E Nithin Computer, Ch Sai Krishna, and Ch Bindhu Madhavi. 2023. Real-Time Face Mask Detection using Computer Vision and Machine Learning. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 1532–1537.

Caitlin Sadowski, Emma Söderberg, Luke Church, Michal Sipko, and Alberto Bacchelli. 2018. Modern Code Review: A Case Study at Google. In 2018 IEEE/ACM 40th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP). 181–190.
Published
2025-09-22
MARQUES, Wendell; BONFIM, Andrezza De Melo; FILHO, Eduardo; VERAS, Edluce Leitao; SOUZA, Daniel; SILVA, Vinicius Gabriel da. Application of Computer Vision to image review process in the software development. In: BRAZILIAN SYMPOSIUM ON SYSTEMATIC AND AUTOMATED SOFTWARE TESTING (SAST), 10. , 2025, Recife/PE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 150-152. DOI: https://doi.org/10.5753/sast.2025.13969.