Thierry Sorg

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Sorg
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Thierry
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thierry.sorg@unisg.ch
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+41 71 224 34 28

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    Item type:Publication,
    Beyond process model complexity: a multi-granular investigation across time and space based on eye-tracking
    (Springer Science and Business Media LLC, 2026-01-24)
    Thierry Sorg  
    ;
    Amine Abbad-Andaloussi  
    ;
    Kindler, Ekkart
    ;
    Barbara Weber  
    Process models support analysis, design, implementation, and operation of information systems and must therefore remain understandable for diverse process stakeholders. Numerous metrics estimate model complexity, yet their ability to predict the mental effort (i.e., cognitive load) users actually experience is still unclear. This study addresses that gap with a controlled eye-tracking experiment involving 27 participants and process models that systematically vary in essential complexity (logic) and accidental complexity (layout). Some of these models combine simple and complex parts. Task complexity is explicitly manipulated so that questions target regions of differing complexity. A coarse-grained level analysis tests how model and task complexity relate to cognitive load. Then, a fine-grained level analysis segments eye-tracking data in space and time while investigating users’ visual behavior and cognitive load during model comprehension. Results show that complexity metrics align with cognitive load when the essential and accidental complexity of process models are well captured, and that the task characteristics affect this relationship. Moreover, they show that distinct phases emerge during process model comprehension and that non-task-relevant regions in process models contribute to cognitive load in a non-uniform way (especially early in a task). These findings strengthen measurement practice in Business Process Management (BPM) by establishing an empirical correspondence between a comprehensive metric suite and multimodeal cognitive load indicators and point to adaptive modeling tools that help focus attention on task-relevant parts of process models.
    Type:Article
    Journal:Process Science
    Volume:3
    Issue:1
    URL:https://link.springer.com/article/10.1007/s44311-025-00035-3
    DOI:10.1007/s44311-025-00035-3
    URI:https://alexandria.unisg.ch/handle/20.500.14171/127944
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    Item type:Publication,
    Process Model Complexity Metrics, Cognitive Load and Visual Behavior: A Multi-granular Eye-Tracking Analysis
    (Springer Nature Switzerland, 2025-06-14)
    Thierry Sorg  
    ;
    Amine Abbad-Andaloussi  
    ;
    Kindler, Ekkart
    ;
    Barbara Weber  
    Complexity metrics are widely used to estimate the difficulty of understanding process models. However, the relationship between these metrics and the concept of cognitive load, which captures the difficulty experienced by users, is not fully understood in the process modeling literature. In neighboring fields like Software Engineering, researchers could only to a limited degree establish a relationship between complexity metrics and users' cognitive load. To investigate the extent to which such a relationship exists in the process modeling field, we conduct an eye-tracking experiment that assesses how a suite of metrics, capturing both the essential complexity inherent to the process specifications and the accidental complexity emerging from the model layout, aligns with users' cognitive load during model comprehension tasks. Our findings show that the used metrics suite aligns well with users' cognitive load. Moreover, our analysis of users' behavior suggests that different levels of model complexity yield distinct visual behaviors. The implications of our work extend to both practice and research, validating a comprehensive suite of complexity metrics and delivering a multi-granular approach that can be reproduced in other experiments to enable the analysis of users' cognitive load and behavior on simple but also complex models.
    Type:journal article
    Journal:Lecture Notes in Business Information Processing
    Volume:558
    URL:https://link.springer.com/chapter/10.1007/978-3-031-95397-2_6
    DOI:10.1007/978-3-031-95397-2_6
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123387
    Scopus© Citations 2
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    Item type:Publication,
    Towards a Fine-grained Analysis of Cognitive Load During Program Comprehension
    (IEEE, 2022)
    Thierry Sorg  
    ;
    Amine Abbad-Andaloussi  
    ;
    Barbara Weber  
    Program comprehension is inherent to all software development activities. This task may require a high mental effort (or so-called “cognitive load”), which in turn can hinder the performance of developers. In the literature, several authors have investigated the ability of biosignals to estimate developers’ cognitive load during program comprehension. While the majority of these studies provide estimates at the task level, we aim for a more fine-grained level of analysis allowing to pinpoint the critical parts of code that could be associated with cognitive load. We infer these critical parts solely from eye fixation features and investigate qualitatively their relationship with those perceived as challenging by users. Being able to pinpoint critical parts in the source-code, is a first stride towards a very handy approach providing targeted support to developers to prevent them from committing errors. Furthermore, such a lightweight approach can be adapted in online settings.
    Type:conference paper
    Journal:2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
    DOI:10.1109/SANER53432.2022.00092
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109544
    Scopus© Citations 10
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    Estimating developers' cognitive load at a fine-grained level using eye-tracking measures
    (IEEE/ACM, 2022)
    Abbad-Andaloussi, Amine  
    ;
    Sorg, Thierry  
    ;
    Weber, Barbara  
    The comprehension of source code is a task inherent to many software development activities. Code change, code review and debugging are examples of these activities that depend heavily on developers' understanding of the source code. This ability is threatened when developers' cognitive load approaches the limits of their working memory, which in turn affects their understanding and makes them more prone to errors. Measures capturing humans' behavior and changes in their physiological state have been proposed in a number of studies to investigate developers' cognitive load. However, the majority of the existing approaches operate at a coarse-grained task level estimating the difficulty of the source code as a whole. Hence, they cannot be used to pinpoint the mentally demanding parts of it. We address this limitation in this paper through a non-intrusive approach based on eye-tracking. We collect users' behavioral and physiological features while they are engaging with source code and train a set of machine learning models to estimate the mentally demanding parts of code. The evaluation of our models returns F1, recall, accuracy and precision scores up to 85.65%, 84.25%, 86.24% and 88.61%, respectively, when estimating the mental demanding fragments of code. Our approach enables a fine-grained analysis of cognitive load and allows identifying the parts challenging the comprehension of source code. Such an approach provides the means to test new hypotheses addressing the characteristics of specific parts within the source code and paves the road for novel techniques for code review and adaptive e-learning.
    Type:conference paper
    DOI:10.1145/3524610.3527890
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109146
    Scopus© Citations 30