# Machine Learning Software Engineering in Practice: An Industrial Case Study

arXiv: https://arxiv.org/abs/1906.07154 · HTML: https://mlsanity.com/paper/1906.07154/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 12 of 12)

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- [Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision](https://mlsanity.com/paper/2303.07476/) (2023)
- [PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2402.00699/) (2024)
- [An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors](https://mlsanity.com/paper/2107.00821/) (2021)
- [Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review](https://mlsanity.com/paper/2102.07574/) (2021)
- [Discrepancies among Pre-trained Deep Neural Networks: A New Threat to Model Zoo Reliability](https://mlsanity.com/paper/2303.02551/) (2023)
- [Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review](https://mlsanity.com/paper/2204.04707/) (2022)
- [Capturing Dependencies within Machine Learning via a Formal Process Model](https://mlsanity.com/paper/2208.05219/) (2022)
- [Engineering an Intelligent Essay Scoring and Feedback System: An Experience Report](https://mlsanity.com/paper/2103.13590/) (2021)

## Abstract (arXiv)

SAP is the market leader in enterprise software offering an end-to-end suite of applications and services to enable their customers worldwide to operate their business. Especially, retail customers of SAP deal with millions of sales transactions for their day-to-day business. Transactions are created during retail sales at the point of sale (POS) terminals and then sent to some central servers for validations and other business operations. A considerable proportion of the retail transactions may have inconsistencies due to many technical and human errors. SAP provides an automated process for error detection but still requires a manual process by dedicated employees using workbench software for correction. However, manual corrections of these errors are time-consuming, labor-intensive, and may lead to further errors due to incorrect modifications. This is not only a performance overhead on the customers' business workflow but it also incurs high operational costs. Thus, automated detection and correction of transaction errors are very important regarding their potential business values and the improvement in the business workflow. In this paper, we present an industrial case study where we apply machine learning (ML) to automatically detect transaction errors and propose corrections. We identify and discuss the challenges that we faced during this collaborative research and development project, from three distinct perspectives: Software Engineering, Machine Learning, and industry-academia collaboration. We report on our experience and insights from the project with guidelines for the identified challenges. We believe that our findings and recommendations can help researchers and practitioners embarking into similar endeavors.