What started as TNO-ESI research into analyzing and transforming sprawling software codebases has grown into a commercial software modernization offering from Capgemini. Generative AI is now opening up new opportunities. “Software modernization usually isn’t a core activity for high-tech companies,” notes Niels Brouwers, solution architect for augmented software engineering at Capgemini. “Their core activity is to create new microscopes, new wafer scanners or new image-guided therapy systems.” Keeping decades-old software up to date is essential, but as Brouwers puts it, “software modernization often is just a burden for them.” That problem was the starting point for Renaissance , technology developed at TNO-ESI to analyze large software systems and perform automated transformations. Early work included a project at Thermo Fisher Scientific aimed at untangling build dependencies. Later projects with other ESI research partners, including Philips and VDL ETG, broadened its use from analysis to large-scale code cleanup, refactoring and architectural checks. “We can use static analysis and specifically the Renaissance code graph to ground answers that LLMs can give about the code,” says TNO-ESI’s Rosilde Corvino. Credit: TNO-ESI/Bram Saeys At the heart of Renaissance are parsers, static analysis and a configurable graph-based representation that integrates software information across multiple abstraction levels. “This ranges from directories, files, classes and functions to libraries, executables, interfaces and architectural components,” explains Rosilde Corvino, senior research fellow at TNO-ESI and technical lead of several projects based on the technology. “Every night, Renaissance extracts this information and combines it in a graph representing the software system.” This system view not only provides better insight, but it also speeds up analysis and enables automated software transformations. Engineers can identify a recurring pattern in the graph and use Renaissance to locate and transform occurrences throughout a codebase. The economics become particularly attractive when a relatively simple modification has to be repeated hundreds or thousands of times. VDL ETG provided an early example. The company’s software stack contained support for hardware that was no longer present, creating unnecessary maintenance work. Renaissance was used to investigate cleaning up the code and to address large numbers of warnings reported by Tiobe tooling. “You have to clean up the code then in hundreds, maybe thousands of files, which is typically very effort-intensive if you want to do that manually,” says Jos Hegge, who represents TNO-ESI’s tooling in bringing research results into industrial applications and also manages several of its projects. Ten years of research in software maintenance and evolution. Credit: TNO-ESI Progressive automation Industrial experience also exposed a weakness: Renaissance was powerful but difficult to use. “Engineers had to understand its database schema, its query language and the internal representation produced by its parsers,” Corvino illustrates. “There was a very steep learning curve.” TNO-ESI and its partners experimented with training developers and architects to use the technology themselves. The conclusion was that specialist teams could make more economic sense. Capgemini reached a similar verdict from its industrial work. “Pushing these technologies doesn’t work,” adds Brouwers. “They’re too complicated, too generic. They need too much tailoring to the customer’s environment.” Renaissance also pays off primarily when transformations can be performed at sufficient scale to outweigh the investment in setting them up. Bits&Chips’ Dutch state of semicon 2026 This article is part of the Bits&Chips special edition on the Dutch state of semicon, published on 23 October 2026. Order your hard copy of the special at info@techwatch.nl. That insight helped shape the Genie team at Philips’ Image Guided Therap