The introduction of AI into the developer workflow has fundamentally changed the conversation around productivity, but merely adopting tools is not enough. Drawing on insights from the 2025 DORA State of AI-assisted Software Development report, this keynote argues that AI is an amplifier that magnifies an organization's pre-existing technical and cultural capabilities. It will show why the greatest returns on AI investment come not from the models themselves, but from the maturity of the underlying software delivery system. We will explore a real-world transformation, using the journey of Unico IDTech https://unico.io as a prime example. This story demonstrates how creating a robust and consistent engineering structure Dev Prime coupled with a radical shift to user-centric monitoring SLOs and reliability engineering created the necessary high-quality Platform foundation. When this foundation is present-characterized by clear governance, healthy data ecosystems, and foundational practices-AI's benefits such as throughput and organizational performance gains are dramatically amplified. This session provides technology leaders with a clear, data-driven mandate: to maximize the value of AI, you must first invest in and rationalize your foundational structure, treating your internal platform as the critical enabler for sustainable AI-driven excellence.
How does fault tolerance affect latency in a system? This talk examines this issue in the context of crash faults for vote-based consensus in blockchains. Specifically, the timers that are often used for fault tolerance can make consensus time non-monotonic with respect to timer value and number of faults. Also, the protocol design for fault tolerance can affect its performance scalability, but this depends on the underlying network topology.
Neural Code Models (NCMs) are rapidly progressing from research prototypes to commercial developer tools. As such, understanding the capabilities and limitations of such models is becoming critical. However, the abilities of these models are typically measured using automated metrics that often only reveal a portion of their real-world performance. While, in general, the performance of NCMs appears promising, currently much is unknown about how such models arrive at decisions or whether practitioners trust NCMs' outcomes. In this talk, I will introduce doCode, a post hoc interpretability framework specific to NCMs that can explain model predictions. doCode is based upon causal inference to enable programming languageoriented explanations. While the theoretical underpinnings of doCode are extensible to exploring different model properties, we provide a concrete instantiation that aims to mitigate the impact of spurious correlations by grounding explanations of model behavior in properties of programming languages. doCode can generate causal explanations based on Abstract Syntax Tree information and software engineeringbased interventions. To demonstrate the practical benefit of doCode, I will present empirical results of using doCode for detecting confounding bias in NCMs as well as detecting code smells in the code generated by state-of-the-art NCMs, such as CodeLlama and Mistral. Finally, I will showcase results of our recent work on how causal inference can be used in the context of secure software engineering research.
As the use of machine learning ML becomes more prevalent in many practical software systems, maintaining the long-term availability of ML systems has emerged as a new critical challenge. In ML system operations, ML models continuously confront the risk of performance degradation due to dataset shifts or security attacks. As the system performance and quality may deteriorate over time, implementing proper mitigation methods to ensure the high availability of ML systems is essential. This talk introduces recent approaches to enhance the reliability and availability of ML systems through N-version configuration, software rejuvenation, and ML model updates. While these methods are based on wellestablished techniques, several unique perspectives, specifically tailored for ML-based systems, are discussed. The talk will also highlight future challenges for high-availability ML systems, which may consist of multiple dependent ML and AI models.
The growing sophistication of cyber threats demands dynamic and adaptive security approaches, especially in cloud computing environments where shared resources create a large attack surface. In this context, Moving Target Defense (MTD) emerges as a promising paradigm that hinders the predictability of the infrastructure by dynamically altering its properties. This paper presents a practical analysis of the MTD technique, implemented through virtual machine (VM) migration in an environment managed by Apache CloudStack. The objective is to evaluate its effectiveness as a proactive security strategy against distinct attack vectors, including Distributed Denial of Service (DDoS) and microarchitectural stress attacks targeting the CPU. The methodology involved configuring a cloud environment with multiple physical hosts, simulating attacks, and monitoring the system. Preliminary results indicate that MTD contributes to stabilization and reduction of resource consumption (CPU and memory) under attack, reinforcing its potential as a complementary defense strategy to enhance availability.
