Node co-activations as a means of error detection—Towards fault-tolerant neural networks

Context: Machine learning has proved an efficient tool, but the systems need tools to mitigate risks during runtime. One approach is fault tolerance: detecting and handling errors before they cause harm. Objective: This paper investigates whether rare co-activations – pairs of usually segregated nodes activating together – are indicative of problems in neural networks (NN). These could be used to detect concept drift and flagging untrustworthy predictions. Methods: We trained four NNs. For each, we studied how often each pair of nodes activates together. In a separate test set, we counted how many rare co-activations occurred with each input, and grouped the inputs based on whether its classification was correct, incorrect, or whether its class was absent during training. Results: Rare co-activations are much more common in inputs from a class that was absent during training. Incorrectly classified inputs averaged a larger number of rare co-activations than correctly classified inputs, but the difference was smaller. Conclusions: As rare co-activations are more common in unprecedented inputs, they show potential for detecting concept drift. There is also some potential in detecting single inputs from untrained classes. The small difference between correctly and incorrectly predicted inputs is less promising and needs further research.Peer reviewe

Robustness of AutoML for Time Series Forecasting in Sensor Networks

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Testing the Robustness of AutoML Systems

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Command Similarity Measurement Using NLP

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Quantum Software Engineering Challenges from Developers' Perspective: Mapping Research Challenges to the Proposed Workflow Model

Despite the increasing interest in quantum computing, the aspect of development to achieve cost-effective and reliable quantum software applications has been slow. One barrier is the software engineering of quantum programs, which can be approached from two directions. On the one hand, many software engineering practices, debugging in particular, are bound to classical computing. On the other hand, quantum programming is closely associated with the phenomena of quantum physics, and consequently, the way we express programs resembles the early days of programming. Moreover, much of the software engineering research today focuses on agile development, where computing cycles are cheap and new software can be rapidly deployed and tested, whereas in the quantum context, executions may consume lots of energy, and test runs may require lots of work to interpret. In this paper, we aim at bridging this gap by starting with the quantum computing workflow and by mapping existing software engineering research to this workflow. Based on the mapping, we then identify directions for software engineering research for quantum computing.Comment: 4 pages, 1 figur

Practices and Infrastructures for Machine Learning Systems: An Interview Study in Finnish Organizations

Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence engineering approaches, but practices and tools are still needed for the testing and monitoring of ML-enabled systems.Peer reviewe

Regression Test Selection Tool for Python in Continuous Integration Process

In this paper, we present a coverage-based regression test selection (RTS) approach and a developed tool for Python. The tool can be used either on a developer's machine or on build servers. A special characteristic of the tool is the attention to easy integration to continuous integration and deployment. To evaluate the performance of the proposed approach, mutation testing is applied to three open-source projects, and the results of the execution of full test suites are compared to the execution of a set of tests selected by the tool. The missed fault rate of the test selection varies between 0-2% at file-level granularity and 16-24% at line-level granularity. The high missed fault rate at the line-level granularity is related to the selected basic mutation approach and the result could be improved with advanced mutation techniques. Depending on the target optimization metric (time or precision) in DevOps/MLOps process the error rate could be acceptable or further improved by using file-level granularity based test selection.Peer reviewe

On Misbehaviour and Fault Tolerance in Machine Learning Systems

Machine learning (ML) provides us with numerous opportunities, allowing ML systems to adapt to new situations and contexts. At the same time, this adaptability raises uncertainties concerning the run-time product quality or dependability, such as reliability and security, of these systems. Systems can be tested and monitored, but this does not provide protection against faults and failures in adapted ML systems themselves. We studied software designs that aim at introducing fault tolerance in ML systems so that possible problems in ML components of the systems can be avoided. The research was conducted as a case study, and its data was collected through five semi-structured interviews with experienced software architects. We present a conceptualisation of the misbehaviour of ML systems, the perceived role of fault tolerance, and the designs used. Common patterns to incorporating ML components in design in a fault tolerant fashion have started to emerge. ML models are, for example, guarded by monitoring the inputs and their distribution, and enforcing business rules on acceptable outputs. Multiple, specialised ML models are used to adapt to the variations and changes in the surrounding world, and simpler fall-over techniques like default outputs are put in place to have systems up and running in the face of problems. However, the general role of these patterns is not widely acknowledged. This is mainly due to the relative immaturity of using ML as part of a complete software system: the field still lacks established frameworks and practices beyond training to implement, operate, and maintain the software that utilises ML. ML software engineering needs further analysis and development on all fronts.Peer reviewe

Systematic literature review of validation methods for AI systems

Context: Artificial intelligence (AI) has made its way into everyday activities, particularly through new techniques such as machine learning (ML). These techniques are implementable with little domain knowledge. This, combined with the difficulty of testing AI systems with traditional methods, has made system trustworthiness a pressing issue. Objective: This paper studies the methods used to validate practical AI systems reported in the literature. Our goal is to classify and describe the methods that are used in realistic settings to ensure the dependability of AI systems. Method: A systematic literature review resulted in 90 papers. Systems presented in the papers were analysed based on their domain, task, complexity, and applied validation methods. Results: The validation methods were synthesized into a taxonomy consisting of trial, simulation, model-centred validation, and expert opinion. Failure monitors, safety channels, redundancy, voting, and input and output restrictions are methods used to continuously validate the systems after deployment. Conclusions: Our results clarify existing strategies applied to validation. They form a basis for the synthesization, assessment, and refinement of AI system validation in research and guidelines for validating individual systems in practice. While various validation strategies have all been relatively widely applied, only few studies report on continuous validation.Peer reviewe

Software Framework for Data Fault Injection to Test Machine Learning Systems

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