On this site, there is only displayed the English speaking sessions of the OOP 2022 Digital. You can find all conference sessions, including the German speaking ones, here.
The times given in the conference program of OOP 2022 Digital correspond to Central European Time (CET).
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Track: Artificial Intelligence Now!
- Artificial Intelligence Now!
- C++ and Programming of Embedded Systems
- DevOps & Automation Now!
- Diversity & Inclusion
- From Projects to Products/Services: Aligning Architecture and Organization for Sustainable Speed
- Full Day Tutorial
- Fusion: IT-Future-Society
- Half Day Tutorial
- Modern Software Architecture
- Product Development in Times of Digitalization
- Signature Track: The Time is Now!
- Social Integration
- Software Architecture Communication & Assessment
- Testing & Quality
- Trends & Techniques
- Use Domain-Driven Design Now!
- Artificial Intelligence
- Domain-Driven Design
- Product Development
- Programming Languages
- Requirements Engineering
- Soft Skills/Social Skills
- Software Engineering
- Talks with Limited Attendance
- Testing & Quality
- Virtual Reality
Today Computer vision has taken a significant spot in our phones, our roads, our markets that we don’t always even recognize if and where it is deployed. Nonetheless, our industries today have so much potential to automate (using CV) their recurrent tasks to reduce costs, while simultaneously increasing quality of the product and efficiency of the process itself. We will learn about some interesting industrial examples which benefit first-hand from simple automation and perhaps get inspired by it.
Target Audience: Software Developers, Data Scientists, AI Engineers, Managers, Project Leader, Decision Makers
In the era of Industry 4.0, we are focusing on digitalizing operations using data. We let cameras ‘see’, detect and classify objects and even take some decisions for us. In this talk, we discuss what is Computer Vision (CV), and learn how could different industries benefit / save recurring costs from it. We will discuss some interesting industrial applications of Computer Vision, for you to get some inspiration on how it can potentially help your company grow, or just to understand the advancements in today’s world.
In these 45 mins, we discuss how CV can:
- automate the industrial processes
- be more then more efficient,
- ensure the product quality,
- or detect anomalies.
Using some demos and videos you’ll get the chance to consolidate the knowledge acquired during the talk.
The adoption of static analysis of C++ and Java requires that the findings and errors can be prioritised in an efficient way. Our work shows that Machine learning (ML) can support this presentation of static analysis results to end-users. The ML engine learns from the codebase itself, and also observes the violations that the user fixes and which he ignores. The ML uses this to suggest the next best violations to fix, relying on probability of violations to be harmful or most likely to be a noise.
Target Audience: Developer Managers, R&D Managers, Software Architects, Software Engineers
Prerequisites: English, Software development, Coding experience, C++, Java, C#.
Static code analysis is often understood as a mandatory part for checking the source code compliance to government and industry regulations, company-wide guidelines and practices. It can play, however, a more fundamental role in estimating the quality of the code in general, understanding the amount of technical debt, creating the strategy to reduce the amount of technical debt, as well as a helper in making decisions on how to speed up the development by creating a more maintainable, understandable, sociable codebase.
However, by its nature, static code analysis is bound to produce a large amount of noise and false alerts that can distract the team from the actual bugs in the code and prevent them from working thoroughly with the findings. One of the reasons for that is the level of soundness of the static analysis tool. If we want to be sure that the analysis is bound to find all errors in the code, the static analysis tool has to report all possible candidates. The more sound the tool is configured to be, the larger the number of the possible errors is reported, which increases the number of false positives as well.
To improve the user experience of working with static analysis technology, we have developed a machine learning (ML) based approach to presenting the results of the static analysis to users. The ML engine can learn from the code base itself, from a user's preferences, as well as from the interaction within the team. At the code level, our engine learns from the syntactical and semantical structure of the analyzed code to understand which violations are more likely to cause more harm, which violations are more likely to be noise, what underlying problems can be fixed to drastically reduce the number of reported violations. At the user level, the ML engine observes which violations the user fixes and which violations the user ignores. Based on these observations, the ML engine builds a model and uses it to suggest the next best violations to fix.
Maintaining a database containing millions of products can be very challenging, especially when the information you require of these products is subject to changes over time.
We show how we used state of the art Deep Learning methods (such as Transformers, BERT) in connection with smart text matching in order to extract relevant information from free-form text.
We also explain how we leveraged the existing database to create an automatically labelled training dataset.
Our model enables us to continuously update idealos database automatically.
Target Audience: Decision Makers, Technical Project Leaders, Developers
Prerequisites: Basic knowledge of machine learning methodology
To maintain idealos product base, product information in the form of values of predefined product attributes needs to be extracted from free-form text product descriptions.
Before the use of a Machine Learning based solution, this process required a lot of manual work to define rules to extract this information. There is also a high effort connected to keeping these rules consistent across the whole database and different types of products, especially since the source of this information (the product descriptions) as well as the required information (the product properties) are subject to changes over time.
In this talk we present a machine learning solution, based on fine tuned state-of-the-art models such as BERT, which is able to extract product information automatically from product descriptions with production-ready performance.
Our solution contains two different models, each following one of the well-known problem settings in Natural Language Processing (NLP): Semantic Segmentation of text (also known as Token Classification) and Question Answering. We will present both models in detail, as well as discussing their advantages and disadvantages for solving the task at hand and how we measured its performance (metrics).
We will also emphasize the importance of identifying aspects of your data that ensure that the developed model can actually fulfill your business needs before curating your dataset.
This highlights another benefit of implementing a Machine Learning model for a huge database: You will get sanity checks of your existing data “for free”, as consistent data is a prerequisite for a successful Machine Learning project.
One problem that is very common in large organisations is that there is often no or only very little training data in the form of labels for specific text sections available. We show how we mitigated this problem by leveraging the existing database to generate a large artificial training dataset. This allowed us to only use a few thousand manually labelled examples for training and testing to reach sufficient performance.