Logo Bits&Chips Event
10 October 2024
Van der Valk Eindhoven-Best
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Exhibition and conference on challenges in complex software engineering, AI in high-tech and system architecture

Gold sponsors

Batenburg Applied Technologies
Capgemini Engineering

Silver sponsors

Accenture Logo
High Tech Institute
Keysight Technologies logo
Thales
TMC logo
VBTI logo

Bronze sponsors

Demcon logo
Hitex logo
ICT Group
Lely logo
Mithun
Verifysoft Technology logo

Partners

Keynote

Chair: Tanja Vos (OU)
09:45-10:30

Sigrid Eldh (Ericsson)

How do you know this is correct?
Testing software in the AI/DevOps era
How do you know this is correct?
Testing software in the DevOps and AI/ML era
Sigrid Eldh (Ericsson)

Industry 4.0 has driven the digitalization of vast amounts of data, leveraging virtual models and simulations, including digital twins, to reduce costs. Now, advanced AI and machine learning (ML) models, particularly deep learning, and generative AI models, eg ChatGPT, are becoming increasingly integral to this process, assisting with tasks and 'open' information. Localized models can make your company's information readily available and ensure data security. However, these models present challenges, including high energy consumption, potential copyright issues and the risk of generating incorrect answers. This raises a critical question: how can we determine what to trust?

In software development, models specially trained for code, are assisting in several tasks, eg redesign and restructuring code, porting code and code generation. When training such multi-modal systems, and utilizing them for software development, we immediately run into predicaments. How can we determine what is correct? The research community is working hard to get these tools to aid in bug localization and automatic correction - which shows the difficulty of determining a precise and correct result.

Ensuring the accuracy of results and the quality of data is paramount. If we train our tests on our previous testing, we might not improve the quality. Answering these questions requires robust skills in testing, test design techniques and test automation - integral components throughout the software DevOps lifecycle, as testing is not just a final step; it happens at every stage of development.  Here, ML can automate and enhance various testing tasks. If we use these models to generate our test, we probably face the same issues as with the code that's being tested. Therefore, fundamental testing skills are more critical than ever. How can we effectively test these models and how can these models be used for testing? How do we determine what to trust and verify as correct? These are the core topics that will be addressed in this talk, providing insights into current best practices and future directions in the field.

Sigrid Eldh is a software industry veteran, with over 40 years in the field. She currently focuses on research in software engineering and testing at Ericsson in Sweden, where she has worked for 30 years. She's passionate about software quality and ways to automate software and testing. She received her PhD on test design from Mälardalen University, where she also works as a senior lecturer. She's also an adjunct professor at Carleton University in Ottawa, Canada. Currently, she's the editor-in-chief for IEEE Software.

Parallel sessions

Engineering automation with AI

Chair: Clara Maathuis (OU)
11:00-11:40

Maliheh Izadi (TU Delft)

Evaluating large language models for code generation - a practical view
Evaluating large language models for code generation: a practical view
Maliheh Izadi (TU Delft)

This talk outlines the practical application and assessment of large language models tailored to the code generation task. It will cover our approach for evaluating these models' performance in real-world software development environments and how we analyzed the failed cases. I will also present promising research areas to address the existing challenges and improving these models.

Maliheh Izadi is an assistant professor at Delft University of Technology, where she directs the AI-enabled Software Engineering research lab. Her research is at the intersection of machine learning and software engineering. Particularly, she studies and develops learning-based models, for source code, to automate software engineering and developer-related tasks. She is an Amazon research award recipient.

11:45-12:25

Anaïs van Asselt (Choco)

La Cucaracha: revolutionizing bug management with AI
La Cucaracha: revolutionizing bug management with AI
Anaïs van Asselt (Choco)

Tired of spending precious time managing bugs? What if half of them don’t even need fixing? At Choco, we faced this challenge by using AI to streamline our bug management process. From automating bug assignment with GPT to integrating Slack, Intercom and Jira with Zapier for seamless bug reporting, our AI-driven hackathons transformed how we handle bugs. Join this talk to discover how AI can not only automate but elevate your bug management, fostering efficiency and creativity in your workflows.

Anaïs van Asselt is a quality coach and test automation enthusiast. With over 10 years of experience in web and back-end test automation across diverse organizations, she collaborates with developers, applying a context-driven approach in pursuit of sustainable automation. Her passion for sustainability, both in test automation and in the broader sense, led her to move to Berlin in 2022 to join Choco, a startup on a mission to reduce global food waste. As one of two quality engineers at Choco, she supports product teams in embedding quality within the SDLC by fostering a quality-first mindset, standardizing test automation and integrating QA solutions into CI/CD pipelines.

AI in the field

Chair: Petra Heck (Fontys)
11:00-11:40

Laurie Bax (VBTI)

Optimize DL models for deployment in the field
Optimize DL models for deployment in the field
Laurie Bax (VBTI)

At VBTI, we have worked on many projects integrating DL models into (robotic) applications, mostly in the agricultural field. Most of these projects have in common that the DL models need to run remotely in fields, have strict timing requirements and have a limited resource budget for inference. While most deep-learning engineers focus on training and evaluating models on workstation-like hardware, this is not enough for the practical applications we're working on. A lot of work is needed to deploy trained models on resource-constrained embedded hardware. In this talk, I will walk you through our process of taking a trained model and deploying it on a device in the field, with taking special care to making the model run as fast as possible using TensorRT.

Laurie Bax is the MLOps lead at VBTI, with over 10 years of experience with deep-learning models. She has been involved in creating applications using these models, ranging from cancer cell detection to harvesting asparagus. Making sure that all tools are available to find the best one has always been important to her, because if the tools work, the rest is easy. She is currently working on the OneDL platform, where she strives to make many different types of state-of-the-art models available on the press of a button, as well as making sure that the models can be tested, deployed and monitored easily in the field.

11:45-12:25

Mahnaz Shokrpour (Canon Production Printing)

Real-time print artifact detection
Real-time print artifact detection
Mahnaz Shokrpour (Canon Production Printing)

Canon Production Printing has always been a pioneer in the inkjet printing business. At CPP, we are proud that our quality excellence put our products in a class of their own. For this to happen, our state-of-the-art image quality control is one of the critical contributors enabling our customers to print job after job with wide range of mixed media and minimum waste, at photographic quality.

In our engines, each second, thousands of nozzles produce millions of very small drops resulting in thousands of printed sheets (or tens of square meters of print) per hour. When only one nozzle performs poorly, even for a second, the resulting printed images can be marked unacceptable in accordance with our high print quality standard. Therefore, a real-time detection of artifacts and initiation of the right follow-up action are crucial to ensure high quality for undisturbed long runs.

In this talk, we will share our ambition and challenges of developing and integrating models (based on machine learning and image processing techniques) for fast and accurate anomaly detection.

Mahnaz Shokrpour joined Canon Production Printing in 2017. In her various roles, she specialized in model-based design, provided innovative solutions for multi-disciplinary challenges and several architectural decompositions for new products. In the past years, she conducted research in diagnostics and machine learning and she is currently leading a team of experts for print artifact detection and is the chair of the machine-learning expert group. She holds a PhD degree from Eindhoven University of Technology in computational mechanics.

Data analytics

Chair: Gwen Calluy (Alten)
11:00-11:40

Elise van der Wielen (Alten)

Sense and Simulations: using simulated data in a sensible way
Sense and Simulations: using simulated data in a sensible way
Elise van der Wielen (Alten)

It is all about data. By now everyone knows data is valuable. We use analytics to understand and even make predictions about our world. But at the core of all these analytics is data.

The amount of data we collect has a direct impact on the accuracy of our predictions, but data collection can be a cumbersome and time-consuming process. Especially with large and complex systems, the effort is huge. Wouldn't it be nice if we could start collecting this valuable data on a system’s behaviour even before the system itself was build?

Data simulation can be a valuable contribution to start testing early and ease the cost and effort of data collection and get right into the analytics. However, there are many challenges and limitations when it comes to statistical models and simulating real-world behavior. It’s about finding the right balance to interpret results. Sometimes you need to dive into the model’s parameters and sometimes you just need to chase that pigeon from the machine.

This talk will share some of the practical experiences at Vanderlande.

With a degree in behavioral psychology from Radboud University and a degree in human-technology Interaction from Eindhoven University of Technology, Elise van der Wielen combines her academic knowledge from social studies and engineering. Being a psychologist, she always had a special interest in the value of new technologies and the impact on our daily lives. As a quality engineer, she worked on improving the verification and validation processes for several startup companies. After some time working in digital forensics, she decided to become a technical consultant at Alten. Currently, she works as a test engineer at Vanderlande, improving the quality of an intelligent shuttle-based automated storage and retrieval system.

11:45-12:25

Annekoos Schaap (Itility)

Tuning your energy usage in house in normal language - using GenAI on top of ‘plain old’ data science
Tuning your energy usage in house in normal language – using GenAI on top of ‘plain old’ data science
Annekoos Schaap (Itility)

In this talk, we will explore the data analytics for Entune BuildingAI. This is a smart assistant to reduce energy consumption at large-scale facilities. We use greybox modeling, while keeping the customer in the loop with a GenAI assistant for feedback and interaction with your energy appliances.

As we dive into the data generated, we focus on what truly matters. How do we gauge performance effectively? What benchmarks ensure our customers’ satisfaction and comfort? These questions have driven us to develop our own set of key performance indicators, while keeping in line with industry standards. Join us to explore how our KPIs not only measure success but also provide actionable insights, ensuring energy efficiency and optimal performance.

Annekoos Schaap holds a bachelor's and master's degree in electrical engineering from Eindhoven University of Technology, where she also specialized in data science. Since joining Itility, she has focused on energy projects, providing valuable insights across various initiatives. Her work includes optimizing energy flows for smart grids, enhancing household energy consumption and optimizing energy consumption for large-scale consumers. Her skill in combining engineering and data science enables innovative energy solutions at Itility.

Software rejuvenation

Chair: Tijs van der Storm (CWI)
11:00-11:40

Bas Beuting (Cordis) & Niels Brouwers (Capgemini Engineering)

Slaying the legacy software beast in the PLC realm
Slaying the legacy software beast in the PLC realm
Bas Beuting (Cordis) & Niels Brouwers (Capgemini Engineering)

Manufacturers are often quite conservative in modernizing and upgrading the PLC systems that keep their factories running. This conservatism is due to the loss of system knowledge and insufficient test coverage, which make it challenging to modernize PLC hardware and make changes in software. In most cases, such changes introduce system failures and unwanted downtime. Did you know manufacturers are scouring the internet for decades-old PLC hardware to prevent factory downtime?

Building on the master project of Bas Beuting, Cordis, Axini and Capgemini have developed an integrated approach to reverse-engineer PLC software to Cordis Suite models, from which PLC code can be generated for a variety of PLC platforms, with functional preservation verified by model-based testing with Axini. In addition, the models secure the essential knowledge to maintain PLC software for the future. No more hoarding of PLC hardware, no more legacy PLC software! Interested to learn more? Join our talk!

Bas Beuting is a software designer at Cordis Suite with an MSc in embedded systems and a BSc in electrical engineering from Eindhoven University of Technology. His master’s thesis focused on converting legacy PLC code into UML-based models, validated by model-based testing. As a former top-level judo athlete, he applies the discipline and drive from his sports career to his professional work. At Cordis Suite, he specializes in developing advanced machine control applications, ensuring long-term maintainability and functionality of industrial software. He is passionate about leveraging innovative technologies to enhance software resilience and efficiency.

Niels Brouwers is a solution architect at Capgemini, with an MSc degree in computer science. He has a strong passion for helping organizations accelerate their software development. This is achieved by increasing the level of abstraction and intensifying the level of automation when developing software. Initially, he did so by applying model-driven engineering approaches; later, he shifted to extracting models out of code bases for the purpose of automated analysis and refactoring.

11:45-12:25

Nan Yang (TNO-ESI)

Leveraging large language models for legacy software
Leveraging large language models for legacy software
Nan Yang (TNO-ESI)

Legacy code is challenging to maintain and understand, yet it is crucial for the operation of complex systems, especially in the Dutch high-tech industry. Static analysis techniques are often used to extract insights from codebases. While reliable, these techniques require parser-specific and domain-specific knowledge, resulting in a steep learning curve that limits their adoption in industrial environment. In contrast, large language models (LLMs) excel in general human-machine interpretation and user-friendliness but lack the precise domain-specific knowledge needed for in-depth accurate code analysis.

At TNO-ESI, we are developing a hybrid method that combines traditional static analysis with LLMs to better support software understanding and development. By using parser-based static analysis, we extract code details into a graph database. We enable LLMs to interact with this graph database to provide natural language responses with accurate knowledge on the codebase. This hybrid approach demonstrates how LLM accuracy can be augmented using traditional static analysis methods and conversely traditional software engineering techniques can be enhanced by integrating abstraction capabilities and user-friendliness of LLMs, allowing users to obtain answers that neither static analysis tools nor LLMs could address independently.

Nan Yang moved to Eindhoven to pursue a master’s in embedded systems at Eindhoven University of Technology (TUE). During her master's graduation project, she collaborated with ASML, and this collaboration extended into her PhD with a joint project between ASML and TUE. She studied model-driven and reverse-engineering techniques with a special focus on legacy systems. Using empirical methods, her research provides insights into software engineering practices and technologies, identifying their limitations and suggesting improvements. After completing her PhD in 2023, she joined TNO-ESI as a research fellow, where she continues to address the challenges of maintaining and evolving complex software systems in the high-tech industry.

Systems engineering

Chair: Ger Schoeber (High Tech Institute)
11:00-11:40

Sezen Acur & Bram van der Sanden (TNO-ESI)

What can systems engineering offer the high-tech equipment industry for continuous innovation?
What can systems engineering offer the high-tech equipment industry for continuous innovation?
Sezen Acur & Bram van der Sanden (TNO-ESI)

Systems engineering complexity in the high-tech equipment industry has increased, as the systems themselves steadily evolved with increased functionality and complexity. Current systems have a long lifetime, undergo rapid technological advancement, are increasingly software-intense and connected to other systems. Growing system complexity, the global competition, successful adoption of new technologies as AI and scarcity of systems engineers provide challenges to continuous innovation of the high-tech equipment industry.

Improved systems architecting and systems engineering (SA/SE) methodologies and competence development are needed to address these challenges. TNO-ESI has created an outlook on the required SA/SE capabilities. This talk provides an overview of identified relevant trends and challenges across the high-tech equipment industry, the derived needs and a research outlook on what is needed to upgrade their systems architecting and systems engineering capabilities.

Sezen Acur is a systems engineer and a research fellow at TNO-ESI. She has educational background in systems engineering and renewable energy management. Before joining TNO in 2021, she worked in the USA as a systems engineer in the defense, space, civil and aviation industry for more than 4 years. In the last three years, she gained experience in the high-tech equipment industry and utilized her expertise in the systems engineering and energy domain of the Nxtgen Hightech program. Her interests include embedding safety and security on large scale complex systems, R&D, solar and hydrogen system management.

Bram van der Sanden is a senior research fellow at TNO-ESI. He has a background in computer science and holds a PhD on performance analysis of supervisory controllers from Eindhoven University of Technology. His current research focus is on model-based system engineering, system performance and modeling and analysis of system behavior.

11:45-12:25

Leon Bouwmeester (Signify)

If reliability was a feature, how would you prioritize it?
If reliability was a feature, how would you prioritize it?
Leon Bouwmeester (Signify)

Early 2024, a major migration of a vital part of the Philips Hue cloud back-end infrastructure was successfully completed. During this migration, one of world’s largest IoT fleets consisting of millions of devices was moved over without any disruption or consumers even noticing it in any shape or form. The main reason behind this success can be attributed to the use of the principles of site reliability engineering (SRE), which is what you get when you treat operations as if it is a software problem.

In this talk, it is explained what SRE is, what the implications are on your product and how it is perceived by the customer, your system architecture, on your engineering processes and even on some of your business processes. It also addresses the various lessons learned the hard way.

Leon Bouwmeester is director of engineering and as head of the Hue platform, he sets and leads the strategic direction for the entire platform infrastructure for Philips Hue, one of the brands of Signify (f.k.a. as Philips Lighting). He focuses on providing the services for identity management, onboarding and IoT to accelerate development of the lighting and security applications. He has almost 30 years of successful experience in embedded software, cloud and IoT in various industries. He holds an MSc degree and a PDEng degree from Delft University of Technology, as well as an MBA degree from Insead.

Keynote

Chair: Tanja Vos (OU)
13:45-14:25

Robert Engels (Capgemini AI Lab)

Techno-realism 101:
a guide to not getting fooled by AI
Techno-realism 101: a guide to not getting fooled by AI
Robert Engels (Capgemini AI Lab)

AI is like a teenager – it's always promising more than it can deliver. But as someone who's been in the AI game for three decades, I've learned to separate the hype from the reality. I've seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I've seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I've also seen the incredible potential of AI to transform our lives and businesses.

In this talk, I'll give you a no-nonsense look at what AI can and can't do, and what we can expect from the future. I'll share my hard-won insights on what works, what doesn't and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let's cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.

Robert Engels is an AI veteran who's been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he's spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he's not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he's not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he's the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.

Parallel sessions

Engineering automation

Chair: Gwen Calluy (Alten)
14:30-15:10

Wytse Oortwijn (TNO-ESI) & Sjoerd Zwart (VDL ETG)

Do you still develop supervisory controllers by hand?!
Do you still develop supervisory controllers by hand?!
Wytse Oortwijn (TNO-ESI) & Sjoerd Zwart (VDL ETG)

Supervisory control is a key part of cyber-physical systems, to orchestrate all system resources to work together in a safe, correct and optimal way. Developing supervisory controllers by hand becomes increasingly challenging, for instance due to increasing performance demands, the need to support more and more system variants, shortage of skilled engineers, and so on. In contrast, with synthesis-based engineering (SBE), correct-by-construction supervisory controllers can automatically be computed based on requirement specifications of what the system should do. These are typically much easier to specify than having to work out how the system should realize its requirements in every possible situation, as is done traditionally. SBE has successfully been applied in industry, for example in the semiconductor domain, the industrial printing domain and the healthcare domain. TNO-ESI is currently investigating the application of SBE together with ASML and VDL ETG, which we will elaborate upon in this talk.

Wytse Oortwijn is a researcher at TNO-ESI. He received his MSc and PhD degrees in computer science from the University of Twente in 2015 and 2019, respectively, specialized in formal methods and tools. Moreover, he has worked as a postdoctoral researcher at ETH Zurich (2019-2020) at the Programming Methodology Group on formal methods for ensuring quality of software systems. His current work at TNO-ESI focuses on making formal methods like synthesis-based engineering applicable in industry.

Sjoerd Zwart is a software architect at VDL ETG T&D. He is responsible for software development projects for customers other than ASML. Has 29 years of experience in the Eindhoven region, working for Philips, ASML, JVH Gaming Products and Metatronics.

15:15-15:55

Maaike van Leuken (TNO)

Automatic formal verification of hardware design
Automatic formal verification of hardware design
Maaike van Leuken (TNO)

The design of (cryptographic) hardware components in a hardware design language (eg VHDL, Verilog) is a manual task and it is hard to prove that the design adheres to the functional and security requirements. Formal verification can be used to provide guarantees on the security of the mathematical design of cryptography or its software implementation. Some formal verification methods are already applied in the field of hardware design, but these are all manual. I will talk about our research into automatic formal verification of hardware designs and how these techniques can even automatically generate the VHDL/Verilog code based on the specification.

After completing her studies on computing science, cyber security and cryptography, Maaike van Leuken joined TNO in 2021 as researcher in the field of post-quantum cryptography. During this time, she has also worked on the hardware security of trusted execution environments and supply chain security of cryptographic hardware devices. Since the beginning of 2024, she is also responsible for the strategy around post-quantum cryptography within TNO.

AI in the field

Chair: Clara Maathuis (OU)
14:30-15:10

Artiom van den Broek (Ministry of Defense)

Project Sentinel: designing and prototyping military autonomous systems at the Royal Netherlands Army
Project Sentinel: designing and prototyping military autonomous systems at the RAS unit of the Royal Netherlands Army
Artiom van den Broek (Ministry of Defense)

The Robotics and Autonomous Systems (RAS) unit of the Royal Netherlands Army is shaping the future of military operations with its groundbreaking Sentinel project. This initiative aims to revolutionize battlefield tactics by integrating advanced unmanned autonomous systems into military scenarios. Sentinel focuses on designing, prototyping and demonstrating a comprehensive chain of functions, including human input, human-machine interfaces, threat and terrain analysis, doctrinal planning, robot control and autonomous execution. Collaborating with both military and civilian industries, the project leverages Agile and Scrum methodologies to create regular demonstrations. Currently, virtual showcases highlight the autonomous capabilities of unmanned ground vehicles (UGVs). Future phases will expand to include diverse operational environments and a variety of UxVs, enhancing their functions to further solidify the operational and tactical advantages of autonomous military robotics.

Artiom van den Broek, a Dutch military officer serving in the Royal Netherlands Army, specializes in the innovative field of robotics and autonomous systems. His expertise lies in applied military autonomy and artificial intelligence for unmanned vehicles. He holds a master’s degree in applied physics, with a specialization in quantum nanoscience, from Delft University of Technology (Faculty of Applied Sciences). Currently in his second year as a PhD candidate, he is focused on designing military autonomous systems at the University of Twente (Faculty of Engineering Technology).

15:15-15:55

Filip Slijkhuis (Thales)

Make your AI say “I don’t know” - an introduction to probabilistic programming
Make your AI say “I don’t know” – an introduction to probabilistic programming
Filip Slijkhuis (Thales)

Computers are deterministic, but the real world is full of unpredictability. In critical decision-making, it is crucial for AI models to reflect this uncertainty. Imagine being part of a coast guard rescue operation. A deterministic AI system could wrongly distinguish between a drifting piece of debris and a person in distress without providing any additional information. If an AI system instead conveyed its uncertainty about the prediction, would you send someone out to check?

Probabilistic programming excels in such scenarios by merging statistical modeling with AI and machine learning, giving models the ability to handle uncertainty effectively. This talk provides an introduction to probabilistic programming, highlighting its crucial role in AI and machine learning for decision-making. It delves into the motivations behind adopting probabilistic programming and explores practical examples and key concepts, such as the Pyro framework, demonstrating the robust and reliable nature of AI systems powered by probabilistic programming.

Filip Slijkhuis is an AI scientist at Thales Netherlands, specializing in deep, convolutional, recurrent and spiking neural networks, as well as variational inference and VAEs. He collaborates with a team of AI experts, leveraging Bayesian reasoning, probabilistic graphical modeling, particle filtering and hybrid machine learning to solve challenges related to data fusion and decision-making in defense, safety and security. As a fresh addition to the AI industry, he holds dual master’s degrees in artificial intelligence and computing science from Radboud University Nijmegen, acquired in 2022.

Software quality

Chair: Anaïs van Asselt (Choco)
14:30-15:10

Klaus Lambertz (Verifysoft)

Static code analysis and dynamic tests - complementary procedures for quality assurance
Static code analysis and dynamic tests – complementary procedures for quality assurance
Klaus Lambertz (Verifysoft)

Static code analysis and runtime tests in conjunction with code coverage are proven methods for improving code quality. This talk shows the advantages but also the limitations of both methods. To ensure good code quality, both processes must be used in a complementary manner. The talk also discusses suitable criteria for selecting tools for static code analysis and measuring code coverage.

Klaus Lambertz was born in Cologne (Germany) and lives in Strasbourg (France) at the French-German border. Prior to founding Verifysoft Technology in 2003, he had sales and management positions with different software testing solution providers in France and Germany (Parasoft, Testlight). He graduated in studies of economics, marketing and foreign trade in Cologne (Germany) and Paris (France). During the last years, he developed Verifysoft from a two-man company into a global provider of software testing tools with over 750 customers in 43 countries.

15:15-15:55

Oliver Dengiz (Hitex)

Facts about code coverage - enlightening and perhaps frightening to some
Facts about code coverage – enlightening and perhaps frightening to some
Oliver Dengiz (Hitex)

Code coverage is a key metric for software quality assurance, mandated by safety standards such as IEC 61508 and ISO 26262. We focus on branch coverage and modified condition/decision coverage (MC/DC), revealing differences in the calculations of measurement tools and the varying results between tools. This talk highlights two key challenges in coverage measurement: calculation oversights and gaps. It also addresses the pitfalls of deriving test cases from source code in the pursuit of 100 percent coverage. Strategies and resources for achieving unattainable 100 percent coverage are explored, along with the lack of standardized nomenclature for coverage metrics, and misconceptions are dispelled. In addition, insights will be shared on how to authenticate the quality of test cases that contribute to improved software reliability. The presentation is designed for both beginners and experts, and includes interactive examples for a comprehensive understanding.

Oliver Dengiz is a recognized expert in embedded systems and microcontrollers. With extensive knowledge of MCAL and self-test libraries and development tools for Traveo, PSoC, Aurix and Arm Cortex, his expertise enhances Hitex’s ability to serve customers. His background of six years working in the electronics industry underscores his skills in business development and customer satisfaction.

Sustainable AI

Chair: Tijs van der Storm (CWI)
14:30-15:10

Arlette van Wissen (Philips)

AI in healthcare: green revolution or carbon conundrum?
AI in healthcare: green revolution or carbon conundrum?
Arlette van Wissen (Philips)

Did you know the healthcare sector contributes 4-6 percent to global greenhouse gas emissions, surpassing even the aviation industry? While AI holds the promise of reducing this footprint (for instance through optimized logistics and predictive maintenance), its own energy and water demands can also contribute to healthcare’s carbon emissions. Join this talk to discuss how to strike the right balance and find out how measuring the AI footprint can guide us toward more sustainable choices for healthcare. We will touch upon best practices for green AI and the innovation efforts at Philips to inspire a greener future.

Arlette van Wissen is a senior AI scientist at Philips, where she is leading the work on responsible and sustainable AI innovation, driving efforts to ensure safe and trustworthy AI by creating tools and implementing ethical guardrails concerning topics like bias, accountability and transparency. She consults on data and AI governance and maturity, as well as on upcoming regulations and standards. In 2023, she was appointed as a member of the Advisory Committee Analytics of the Dutch Ministry of Finance.

15:15-15:55

Ana-Lucia Varbanescu (UT)

The cost(s) of AI: a sustainability story
The cost(s) of AI: a sustainability story
Ana-Lucia Varbanescu (UT)

The incredibly fast advances in hardware and software around machine learning enabled massively large and incredibly complex AI models to be extremely accurate for various tasks, from solving ‘simple’ image classification tasks to generating solutions for equations or coding assignments, and even acting as a sparring partner for various games.

However, all these advances do not come for free: the entire lifecycle of machine learning models requires massive computation, from architectural search to hyperparameter tuning and inference at scale.

This computation, in turn, consumes significant amounts of energy and, ultimately, has a non-negligible impact on the long-term sustainability of the field.

In this talk, we argue we need to define the metrics and design the tools required to assess the sustainability impact of AI models during their entire lifecycle. We present a couple of case studies and illustrate the use of these metrics to assess the sustainability cost associated with these models. Furthermore, by means of simple performance and efficiency models, we also illustrate how to monitor, label and possibly improve the sustainability of machine learning models. We conclude that sustainability models are powerful tools to raise awareness (and possibly alleviate concerns) about the sustainability impact of complex AI models, as long as the communities of AI developers and AI users are willing to adopt and/or endorse them.

Ana-Lucia Varbanescu holds a BSc and MSc degree from Politehnica University in Bucharest, Romania. She obtained her PhD from Delft University of Technology and continued to work as a post-doc researcher in the Netherlands, at TU Delft and VU University Amsterdam. She is a MacGillavry fellow at University of Amsterdam, where she was tenured in 2018 as associate professor. Since 2022, she is a professor at University of Twente. She has been a visiting researcher at IBM TJ Watson, Barcelona Supercomputing Center, Nvidia and Imperial College London. Her research stems from HPC and investigates the use of heterogeneous architectures for high-performance computing, with a special focus on performance and energy efficiency modeling for both scientific and irregular, data-intensive applications. Her latest research focuses on zero-waste computing and systems co-design.

Systems engineering

Chair: Ger Schoeber (High Tech Institute)
14:30-15:10

Shari Finner & Thomas Rooijakkers (TNO)

Immune-system inspired cyber resilience by design
Immune-system inspired cyber resilience by design
Shari Finner & Thomas Rooijakkers (TNO)

With an increased dependence on digital systems and a rise in severe cyber security incidents and threats, it’s increasingly hard – if not impossible – for businesses to keep up in the rat race between attackers and defenders. While cyber security has transformed into an essential prerequisite for businesses to function, the products and systems we need to protect become increasingly complex, dynamic and exposed. A reactive approach to cyber security is no longer feasible, nor sufficient. Society requires cyber-resilient products and systems with the inherent ability to prevent, withstand and recover from incidents. Inherent cyber resilience of products and systems benefits business continuity and lowering the overall lifecycle costs by reducing damages, product updates and recalls. Yet, industry parties involved in systems design and engineering are having difficulty crafting a convincing business case.

In this talk, we will showcase TNO’s current research and vision on cyber-resilient system design, touching upon topics including design and lifecycle management, software verification and validation, and human-centric engineering. In addition, we will discuss and demonstrate the possibilities and limitations of TNO's Self-Healing for Cyber Security (SH4CS) software. Inspired by biological mechanisms in cells and the human immune system, this novel software aims to make products and systems autonomously cope with any type of operational (runtime) abnormalities.

Shari Finner is project manager and portfolio manager on TNO innovations for automating cyber security operations. In her work, the goal is to automate cyber security as much as possible, while keeping the human in (or on) the loop. In addition, she is a great enthusiast for autonomous cyber resilience techniques inspired by the human immune system. These mechanisms will allow society to move toward products and systems that can autonomously protect and heal themselves during runtime.

Thomas Rooijakkers is a cyber security researcher at TNO. He has a keen interest in fuzzing, (automated) vulnerability discovery and software security testing techniques. On top of that, he is a great enthusiast of incorporating proper cybersecurity practices throughout the entire system’s lifecycle, with a particularly strong focus on the DevSecOps paradigm. Or, as he prefers, SecDevOps. After all, it's security first!

15:15-15:55

Steven van der Vlugt (Astron) & Tawfeeq Ahmad (iWave)

High-speed data acquisition and data transport in radio telescopes - a systems engineering use case
High-speed data acquisition and data transport in radio telescopes – a systems engineering use case
Steven van der Vlugt (Astron) & Tawfeeq Ahmad (iWave)

Modern systems such as large-scale distributed radio telescopes face strong requirements in data acquisition in the RF front-end and data transport to the back-end. We will present a use case based on a large-scale distributed radio telescope system. In the radio telescope front-end, the antenna signals are digitized, filtered and pre-processed. The resulting data is transmitted in a streaming manner over Ethernet to a centralized location where multiple antennas are computationally combined into science products.

RFSoC FPGAs provide ample high-speed interfaces. However, design of custom FPGA boards, especially with high-speed interfaces is a labor-intensive and expert task. iWave’s system-on-modules integrate the RFSoC FPGA on a module that can be used in custom designs.

Join us to find out how an RFSoC system-on-module can meet the system challenges of a large-scale distributed radio telescope.

Steven van der Vlugt is a researcher on high-performance computing in the Innovation and Systems department of Astron, the Netherlands Institute for Radio Astronomy. His main work is on system optimization, hardware-software co-design and data transport for real-time signal processing in radio telescope systems. In the past, he worked in similar roles with ASML, Philips Healthcare and Topic.

Tawfeeq Ahmad is associated director product marketing at iWave for the embedded boards and solutions. He holds a Bachelor of Technology from the National Institute of Technology Karnataka and Master of Business Administration from the Northwestern University - Kellogg School of Management.

Keynote

Chair: Tanja Vos (OU)
16:30-17:15

Martin van den Brink (former president & CTO of ASML)

The continuous evolution of imaging-based litho
Techno-realism 101: a guide to not getting fooled by AI
Robert Engels (Capgemini AI Lab)

AI is like a teenager – it's always promising more than it can deliver. But as someone who's been in the AI game for three decades, I've learned to separate the hype from the reality. I've seen the promises of AI revolutionizing industries, only to be met with disappointment and disillusionment. I've seen the overpromising, the underdelivering and the sheer chaos that ensues when AI is not properly implemented. But I've also seen the incredible potential of AI to transform our lives and businesses.

In this talk, I'll give you a no-nonsense look at what AI can and can't do, and what we can expect from the future. I'll share my hard-won insights on what works, what doesn't and what we can expect from the future of AI. No sugarcoating, no hype, just the straight truth about AI. So, buckle up and get ready for a dose of AI realism! Let's cut through the noise and get to the heart of what AI can really do for us. Join me for a candid conversation about the future of AI and what it means for our businesses, our lives and our world.

Robert Engels is an AI veteran who's been around since the dawn of the digital age (1986, to be exact). With a PhD in machine learning from the Technical University of Karlsruhe (now KIT), he's spent decades mastering the dark arts of artificial intelligence, semantic technology, logics and reasoning in the context of machine learning and AI. When he's not busy being a strategic advisor to company boards or upper management, he can be found architecting AI-based tech programs for the likes of Bayer, Mercedes Benz or the Norwegian Broadcasting Corporation. And when he's not saving the world with AI, he can be found speaking at keynotes or publishing articles on the latest and greatest in the crosshairs of technology, psychology, sociology and geopolitics. In short, he's the real deal – a true AI ninja with a PhD and a passion for making the impossible possible.

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Visitor profile

Managing directors • Technical managers • Team leads • Project managers • System architects • Software architects • Software developers • Solution providers • Researchers • Technology innovators

Target industries

Aerospace • Agro & food • Automotive • Consumer electronics • Defense • Factory automation • Healthcare • Industrial systems • Logistics • Semicon

Past attendees

Accenture • Alten • ASM • ASML • Axelera AI • Bosch • Canon Production Printing • Capgemini Engineering • Demcon • ICT Group • Imec • Kulicke & Soffa • Lely • Nearfield Instruments • Neways • Nexperia • NXP • Philips • Priva • Prodrive • Signify • Sioux • Technolution • Thales • Thermo Fisher Scientific • TMC • Tomtom • Vanderlande • VDL

Contact

Logo Bits&Chips Event
10 October 2024
Van der Valk
Eindhoven-Best
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