An Experience Report on Applying Passive Learning in a Large-Scale Payment Company

Wieman, Rick; Aniche, Maurício; Lobbezoo, Willem; Verwer, Sicco; Deursen, Arie van

doi:10.1109/icsme.2017.71

Cited by 10 publications

(8 citation statements)

References 33 publications

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“…In addition, given the caused permissive verification is perceived as a risk by our interviewees, we suggest proposing possible alternatives to SSSM-IMs by investigating the order in which events are actually being called during system operation. One can consider analysing the execution traces of the generated code with pattern mining techniques widely studied in the field of model learning (Yang et al 2019;Wieman et al 2017;Aslam et al 2018), specification mining (Lemieux et al 2015;Lo et al 2011) andprocess mining (van der Aalst 2011;van der Werf et al 2009;Gupta et al 2018).…”

Section: H4mentioning

confidence: 99%

Single-state state machines in model-driven software engineering: an exploratory study

et al. 2021

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Context Models, as the main artifact in model-driven engineering, have been extensively used in the area of embedded systems for code generation and verification. One of the most popular behavioral modeling techniques is the state machine. Many state machine modeling guidelines recommend that a state machine should have more than one state in order to be meaningful. However, single-state state machines (SSSMs) violating this recommendation have been used in modeling cases reported in the literature. Objective We aim for understanding the phenomenon of using SSSMs in practice as understanding why developers violate the modeling guidelines is the first step towards improvement of modeling tools and practice. Method To study the phenomenon, we conducted an exploratory study which consists of two complementary studies. The first study investigated the prevalence and role of SSSMs in the domain of embedded systems, as well as the reasons why developers use them and their perceived advantages and disadvantages. We employed the sequential explanatory strategy, including repository mining and interview, to study 1500 state machines from 26 components at ASML, a leading company in manufacturing lithography machines from the semiconductor industry. In the second study, we investigated the evolutionary aspects of SSSMs, exploring when SSSMs are introduced to the systems and how developers modify them by mining the largest state-machine-based component from the company. Results We observe that 25 out of 26 components contain SSSMs. Our interviews suggest that SSSMs are used to interface with the existing code, to deal with tool limitations, to facilitate maintenance and to ease verification. Our study on the evolutionary aspects of SSSMs reveals that the need for SSSMs to deal with tool limitations grew continuously over the years. Moreover, only a minority of SSSMs have been changed between SSSM and multiple-state state machine (MSSM) during their evolution. The most frequent modifications developers made to SSSMs is inserting events with constraints on the execution of the events. Conclusions Based on our results, we provide implications for developers and tool builders. Furthermore, we formulate hypotheses about the effectiveness of SSSMs, the impacts of SSSMs on development, maintenance and verification as well as the evolution of SSSMs.

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Section: H4mentioning

confidence: 99%

Single-state state machines in model-driven software engineering: an exploratory study

et al. 2021

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“…Logs are also used to build models of the software system. Tools such as Synoptic [10] and DFASAT [30,57] devise inite state machines that represent a software, based on its logs. And given that logs are often ordered in a timely manner, related work also has explored temporal invariant inference [10,41].…”

Section: Related Workmentioning

confidence: 99%

“…The analysis of logs is a widespread practice that has been studied in many diferent contexts. By leveraging log data, researchers were able to help development teams with process mining [15,29,53], anomaly detection [9,24,28,60,61], passive learning [57], fault localization [58,65], invariant inference [10], performance diagnosis [33,40,49,50,64], online trace checking [5], and behavioural analysis [4,43,62].…”

Section: Introductionmentioning

confidence: 99%

Monitoring-aware IDEs

Winter¹,

Aniche

Cito

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Engineering modern large-scale software requires software developers to not solely focus on writing code, but also to continuously examine monitoring data to reason about the dynamic behavior of their systems. These additional monitoring responsibilities for developers have only emerged recently, in the light of DevOps culture. Interestingly, software development activities happen mainly in the IDE, while reasoning about production monitoring happens in separate monitoring tools. We propose an approach that integrates monitoring signals into the development environment and worklow. We conjecture that an IDE with such capability improves the performance of developers as time spent continuously context switching from development to monitoring would be eliminated. This paper takes a irst step towards understanding the beneits of a possible Monitoring-Aware IDE. We implemented a prototype of a Monitoring-Aware IDE, connected to the monitoring systems of Adyen, a large-scale payment company that performs intense monitoring in their software systems. Given our results, we irmly believe that Monitoring-Aware IDEs can play an essential role in improving how developers perform monitoring. CCS CONCEPTS • Software and its engineering → Integrated and visual development environments;

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“…Two automata parts (top, bottom) comparing different behaviors of card types during payments on a pin entry device with the same firmware, learned from a random sample of 5000 transactions. For details see our companion industry track paper [36]. Using a default heuristic, such automata are typically learned within a few seconds.…”

Section: Related Workmentioning

confidence: 99%

“…The core algorithm is efficient and mature. We have applied it ourselves to a range of applications such as software bug discovery [36], modeling network traffic [37], and time series regression [24]. Now is the time to collect user feedback and improve the user experience, including the visualization and customization capabilities.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

flexfringe: A Passive Automaton Learning Package

Verwer

Hammerschmidt

2017

2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Abstract-Finite state models, such as Mealy machines or state charts, are often used to express and specify protocol and software behavior. Consequently, these models are often used in verification, testing, and for assistance in the development and maintenance process. Reverse engineering these models from execution traces and log files, in turn, can accelerate and improve the software development and inform domain experts about the processes actually executed in a system. We present flexfringe, an open-source software tool to learn variants of finite state automata from traces using a state-of-the-art evidence-driven state-merging algorithm at its core. We embrace the need for customized models and tailored learning heuristics in different application domains by providing a flexible, extensible interface.

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An Experience Report on Applying Passive Learning in a Large-Scale Payment Company

Cited by 10 publications

References 33 publications

Single-state state machines in model-driven software engineering: an exploratory study

Single-state state machines in model-driven software engineering: an exploratory study

Monitoring-aware IDEs

flexfringe: A Passive Automaton Learning Package

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