Beware the evolving ‘intelligent’ web service! an integration architecture tactic to guard AI-first components

Cummaudo, Alex; Barnett, Scott; Vasa, Rajesh; Grundy, John; Abdelrazek, Mohamed

doi:10.1145/3368089.3409688

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…Modern software systems, especially ML-based software systems, have a continuously evolving architecture. The architecture of these systems is expected to evolve at run-time continuously [18], [37], [38]. This is primarily because more data becomes available as time progresses, along with the availability of newer and better algorithms.…”

Section: Architecture Evolutionmentioning

confidence: 99%

Software Architecture for ML-based Systems: What Exists and What Lies Ahead

Muccini¹,

Vaidhyanathan²

2021

Preprint

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The increasing usage of machine learning (ML) coupled with the software architectural challenges of the modern era has resulted in two broad research areas: i) software architecture for ML-based systems, which focuses on developing architectural techniques for better developing ML-based software systems, and ii) ML for software architectures, which focuses on developing ML techniques to better architect traditional software systems. In this work, we focus on the former side of the spectrum with a goal to highlight the different architecting practices that exist in the current scenario for architecting ML-based software systems. We identify four key areas of software architecture that need the attention of both the ML and software practitioners to better define a standard set of practices for architecting ML-based software systems. We base these areas in light of our experience in architecting an ML-based software system for solving queuing challenges in one of the largest museums in Italy.

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Section: Architecture Evolutionmentioning

confidence: 99%

Software Architecture for ML-based Systems: What Exists and What Lies Ahead

Muccini¹,

Vaidhyanathan²

2021

Preprint

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“…In this paper we present Threshy 3 , a tool to assist developers in selecting decision thresholds when using intelligent services. The motivation for developing Threshy arose from our work across a set of industry projects, and is an implemented example of the threshold tuner component presented in our complementing ES-EC/FSE 2020 architecture tactic publication [5]. While Threshy has been designed to specifically handle pre-trained classification ML models where the hyperparameters cannot be tuned, the overall conceptual design serves as inspiration for general model calibration.…”

Section: What's My Decision Boundary? (2) Monitoringmentioning

confidence: 99%

Threshy: Supporting Safe Usage of Intelligent Web Services

Cummaudo¹,

Barnett²,

Vasa³

et al. 2020

Preprint

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Increased popularity of 'intelligent' web services provides endusers with machine-learnt functionality at little effort to developers. However, these services require a decision threshold to be set which is dependent on problem-specific data. Developers lack a systematic approach for evaluating intelligent services and existing evaluation tools are predominantly targeted at data scientists for pre-development evaluation. This paper presents a workflow and supporting tool, Threshy, to help software developers select a decision threshold suited to their problem domain. Unlike existing tools, Threshy is designed to operate in multiple workflows including pre-development, pre-release, and support. Threshy is designed for tuning the confidence scores returned by intelligent web services and does not deal with hyper-parameter optimisation used in ML models. Additionally, it considers the financial impacts of false positives. Threshold configuration files exported by Threshy can be integrated into client applications and monitoring infrastructure. Demo: https://bit.ly/2YKeYhE. CCS CONCEPTS• Information systems → Web services; • Computing methodologies → Artificial intelligence; • Software and its engineering → Designing software; Software post-development issues.

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“…Best practice recommends setting up alerting and monitoring infrastructure [2,3,18] and ML specific architectural tactics [4].…”

Section: Introductionmentioning

confidence: 99%

MLGuard: Defend Your Machine Learning Model!

Wong,

Barnett,

Rivera-Villicana

et al. 2023

Proceedings of the 1st International Workshop on Dependability and Trustworthiness of Safety-Critical Systems With Machine Lear

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Machine Learning (ML) is used in critical highly regulated and high-stakes fields such as finance, medicine, and transportation. The correctness of these ML applications is important for human safety and economic benefit. Progress has been made on improving ML testing and monitoring of ML. However, these approaches do not provide i) pre/post conditions to handle uncertainty, ii) defining corrective actions based on probabilistic outcomes, or iii) continual verification during system operation. In this paper, we propose MLGuard, a new approach to specify contracts for ML applications. Our approach consists of a) an ML contract specification defining pre/post conditions, invariants, and altering behaviours, b) generated validation models to determine the probability of contract violation, and c) an ML wrapper generator to enforce the contract and respond to violations. Our work is intended to provide the overarching framework required for building ML applications and monitoring their safety. CCS CONCEPTS• Software and its engineering → Software creation and management; • Computing methodologies → Machine learning.

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Beware the evolving ‘intelligent’ web service! an integration architecture tactic to guard AI-first components

Cited by 12 publications

References 21 publications

Software Architecture for ML-based Systems: What Exists and What Lies Ahead

Software Architecture for ML-based Systems: What Exists and What Lies Ahead

Threshy: Supporting Safe Usage of Intelligent Web Services

MLGuard: Defend Your Machine Learning Model!

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