Using AI-based Analytics for the Improvement & Maintenance of Software Coding


As the requirements of customers for technology products in the mobility sector increase, software development projects become more complex and harder to control. This demands a constant focus on maintaining and writing code, because even small changes in the code affect the overall performance of the technology for the customer.

Almost every industry is trying to incorporate digital technologies and offer better services for its customers. The automotive industry is no exception. Software-driven products such as advanced driver assistance systems will be future market differentiators in the mobility ecosystem. In this context, Embold Technologies is using AI-based software analytics to improve the quality of software products. Their approach aims to achieve a high software quality and less Software Technical Debt. The Bosch software development team in India is responsible for multiple automotive and self-driving technology products and supports project teams from across the world. Due to the ambitious project timelines and the complexity of software projects, it is challenging for the developers to write clean code in the early stages of a project.

Any deviations in the software quality has consequences for the performance of the technology product. Therefore, the challenge for Bosch was to monitor the design and code of software projects regularly and assess relevant code metrics. Ideally, the assessment should be performed automatically during the development stream of the software in order to tackle the problem of Software Technical Debt. The objective of the partnership between Bosch and Embold Technologies was to implement a platform, which provides recommendations and support for software developers, enabling them to write “clean code” while increasing visibility.

The company’s USP lies in how it enables software development teams to quickly foster a clean coding culture, leading to quality gains and positive business impacts. Their advanced quality monitoring and analytics platform analyses software quality and identifies risk areas with objective measurements. Use cases include defect detection, continuous monitoring and developer support.

The Project

The PoC lasted for roughly six months with the Embold and Bosch teams jointly deciding to approach the PoC in two parts. Firstly, they selected several programs related to driver assistance and passive safety systems to leverage Embold’s analysis framework. The setup and the configurations of the Embold platform took three weeks before a first successful code scan was performed during the PoC.


We would like to express our sincere thanks to Soj Thomas(MS/ESW-XC, BOSCH), Hari Prasad Tilak Swamy (MS/PJ-SWO- CX, BOSCH), Akansha Yadav (Embold), Allison Ember (Embold), Sudarshan Bhide (Embold) and Vishal Rai (Embold).


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Plug and Play Tech Center, Mercedes-Benz AG, ARENA2036, University of Stuttgart, ZF, DXC Technology, Porsche, BASF, Webasto, Motherson, DPDHL, Bosch, Schaeffler, STMicroelectronics, Murata, AGC, Hyundai, Linde, BP, Wieland, Faurecia, Eberspächer, ADAC, Sekisui, Plastic Omnium, Zenzic, Maxion, Novelis, ITT, Grupo Antolin, Huf Group, CEAT

Contact for this project

Alina Biermann, Ventures Associate, STARTUP AUTOBAHN powered by Plug and Play,

Sebastian Tietz, Open Bosch Mobility, Bosch,

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