Debugging IoT applications is challenging due to the hardware constraints of IoT devices, making advanced techniques like record-replay debugging impractical. As a result, programmers often rely on manual resets or inefficient and time-consuming debugging techniques such as printf. Although simulators can help in that regard, their applicability is limited because they fall short of accurately simulating and reproducing the runtime conditions where bugs appear. In this work, we explore a novel debugging approach called event-based out-of-place debugging in which developers can capture a remotely running program and debug it locally on a (more powerful) machine. Our approach thus provides rich debugging features (e.g., step-back) that normally would not run on the hardware restricted devices. Two different strategies are offered to deal with resources which cannot be easily transferred (e.g., sensors): pull-based (akin to remote debugging), or push-based (where data updates are pushed to developer’s machine during the debug session). We present EDWARD, an event-based out-of-place debugger prototype, implemented by extending the WARDuino WebAssembly microcontroller Virtual Machine, that has been integrated into Visual Studio Code. To validate our approach, we show how our debugger helps uncover IoT bugs representative of real-world applications through several use-case applications. Initial benchmarks show that event-based out-of-place debugging can drastically reduce debugging latency.

Apache Spark is a framework widely used for writing Big Data analytics applications that offers a scalable and fault-tolerant model based on rescheduling failing tasks on other nodes. While this is well-suited for hardware and infrastructure errors, it is not for application errors as they will reappear in the rescheduled tasks. As a result, applications are killed, losing all the progress and forcing developers to restart them from scratch. Despite the popularity of such a failure-recovery model, understanding and debugging Spark-like applications remain challenging. When an error occurs, developers need to analyze huge log files or undergo time-consuming replays to find the bug. To address these concerns, we present an online debugging approach tailored to Big Data analytics applications. Our approach includes local debugging of remote parallel exceptions through dynamic local checkpoints, extended with domain-specific debugging operations and live code updating functionality. To deal with data-cleaning errors, we extend our model to easily allow developers to automatically ignore exceptions that happen at runtime. We validate our solution through performance benchmarks that show how our debugging approach is comparable or better than state-of-the-art debugging solutions for Big Data. Furthermore, we conduct a user study to compare our approach with another state-of-the-art debugging approach, and results show a lower time to find the solution to a bug using our approach, as well as a generally good perception of the features of the debugger.

Debugging applications that execute within a framework is not always easy: the call-stack offered to developers is often a mix-up of stack frames that belong to different frameworks, introducing an unnecessary noise that prevents developers from focusing on the debugging task. Moreover, relevant application code is not always available in the call-stack because it may have already returned, or is available in another thread. In such cases, manually gathering all relevant information from these different sources is not only cumbersome but also costly.

Many frameworks exist for programmers to develop and deploy Big Data applications such as Hadoop Map/Reduce and Apache Spark. However, very little debugging support is currently provided in those frameworks. When an error occurs, developers are lost in trying to understand what has happened from the information provided in log files. Recently, new solutions allow developers to record & replay the application execution, but replaying is not always affordable when hours of computation need to be re-executed. In this paper, we present an online approach that allows developers to debug Big Data applications in isolation by moving the debugging session to an external process when a halting point is reached. We introduce IDRA MR , our prototype implementation in Pharo. IDRA MR centralizes the debugging of parallel applications by introducing novel debugging concepts, such as composite debugging events, and the ability to dynamically update both the code of the debugged application and the same configuration of the running framework. We validate our approach by debugging both application and configuration failures for two driving scenarios. The scenarios are implemented and executed using Port, our Map/Reduce framework for Pharo, also introduced in this paper.