Joel Huselius

Replay debugging of real-time systems using time machines

In this paper we present a new approach to deterministic replay using standard components. Our method facilitates cyclic debugging of real-time systems with industry standard real-time operating systems using industry standard debuggers. The method is based on a number of new techniques: A new marker for deterministic differentiation between e.g., loop iterations for deterministic reproduction of interrupts and task preemptions, an algorithm for finding well-defined starting points of replay sessions, as well as a technique for using conditional breakpoints in standard debuggers to replay the target system. We also propose and discuss different methods for deterministic monitoring, and provide benchmarking results from an industrial strength case study demonstrating the feasibility of our method. Previously published solutions to the problem of debugging real-time systems have been based on the concept of deterministic replay: where significant system events like task-switches of multitasking software and external inputs are recorded during run-time, and later replayed (re-executed) off-line. Previous works have been based on either non-standard hardware, specially designed compilers or modified real-time operating systems. The reliance on non-standard components has limited the success of the approach. Even though this idea has been around for 20 years, no industrial application for debugging of real-time systems of the method has been presented.

Model synthesis for real-time systems

In this paper, we present a method for model synthesis. Based on observations of running system, a model that can describe the observed behavior is automatically generated. This allows faster and more accurate modeling of existing systems. The models can be used for impact analysis, verification, documentation etc. The method has been implemented; we describe that implementation and present an evaluation of its performance, the conclusion of the evaluation is in favor of the proposed method.

Extracting Simulation Models from Complex Embedded Real-Time Systems

A modeling process is presented for extracting timing-accurate simulation models from complex embedded real-time systems. The process is supported by two complementary methods for tool-supported model extraction, Model Synthesis and Hybrid Model Extraction. The generated models enable impact analysis for complex real-time systems with respect to dynamic system properties, such as timing and resource usage. This can make software mainetnance more predictable with respect to time-to-market and development costs, since timing errors can be identified early and avoided. The contribution of the paper is the modeling process, the Hybrid Model Extraction method and an interactive modeling tool, MASS, designed to support Hybrid Model Extraction of large implementations in C.

Automatic Generation and Validation of Models of Legacy Software

The modeling approach is not used to its full potential in maintenance of legacy systems. Often, models do not even exist. The main reasons being that the economic implications and practical hurdles in manually maintaining models of in-use legacy systems are considered too high by the industry. In this paper, we present a method for automated validation of models automatically generated from recordings of executing real-time embedded systems. This forms an essential constituent of a unified process for the automatic modeling of legacy software. We also present a study in which we automatically model a state-of-practice industrial robot control system, the results of which are clearly positive indicators of the viability of our approach

Evaluating the Quality of Models Extracted from Embedded Real-Time Software

Due to the high cost of modeling, model-based techniques are yet to make their impact in the embedded systems industry, which still persist on maintaining code-oriented legacy systems. Re-engineering existing code-oriented systems to fit model-based development is a risky endeavor due to the cost and efforts required to maintain correspondence between the code and model. We aim to reduce the cost of modeling and model maintenance by automating the process, thus facilitating model-based techniques. We have previously proposed the use of automatic model extraction from recordings of existing embedded real-time systems. To estimate the quality of the extracted models of timing behavior, we need a framework for objective evaluation. In this paper, we present such a framework to empirically test and compare extracted models, and hence obtain an implicit evaluation of methods for automatic model extraction. We present a set of synthetic benchmarks to be used as test cases for emulating timing behaviors of diverse systems with varying architectural styles, and extract automatic models out of them. We discuss the difficulties in comparing response time distributions, and present an intuitive and novel approach along with associated algorithms for performing such a comparison. Using our empirical framework, and the comparison algorithms, one could objectively determine the correspondence between the model and the system being modeled