On Cognitive Biases in Architecture Decision Making

Zalewski, Andrzej; Borowa, Klara; Ratkowski, Andrzej

doi:10.1007/978-3-319-65831-5_9

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…One of these factors are cognitive biases [18]. Their influence on architectural decision-making has been shown as significant in recent research [14] [18] [8] [19]. When no debiasing interventions are applied, the consequences of such biased decisions can be severe -for example resulting in taking on harmful Architectural Technical Debt [1].…”

Section: Related Workmentioning

confidence: 99%

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Is knowledge the key? An experiment on debiasing architectural decision-making -- a pilot study

Borowa,

Dwornik,

Zalewski

2021

Preprint

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The impact of cognitive biases on architectural decisionmaking has been proven by previous research. In this work, we endeavour to create a debiasing treatment that would minimise the impact of cognitive biases on architectural decision-making. We conducted a pilot study on two groups of students, to investigate whether a simple debiasing presentation reporting on the influences of cognitive biases, can provide a debiasing effect. The preliminary results show that this kind of treatment is ineffective. Through analysing our results, we propose a set of modifications that could result in a better effect.

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Section: Related Workmentioning

confidence: 99%

“…Based on the cognitive biases researched previously in relation to software development [10], as well as biases shown previously as influencing software architecture [19], [1], [18], we selected three cognitive biases as the subject of the experiment:…”

Section: Bias Selectionmentioning

confidence: 99%

Is knowledge the key? An experiment on debiasing architectural decision-making -- a pilot study

Borowa,

Dwornik,

Zalewski

2021

Preprint

Self Cite

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“…t r i g g er F l ag ∈ {st ar t , susp, t er m} (14) If the t r i g g er F l ag value consists of an identifier of an initial condition that is defined in F SM d y , then the initial condition is triggered (15)- (17). This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: ) Dynamic Agent Classmentioning

confidence: 99%

“…The above problems concern various applications of robots; hence, robotic systems are complicated and require a straightforward development method. The modeldriven engineering approach, which is based on the model concept, facilitates the design procedure of a system and prevents the influence of cognitive biases [17] on the design of the system architecture at the initial phase [18].…”

Section: Introductionmentioning

confidence: 99%

Scheduling of a Robot’s Tasks With the TaskER Framework

Dudek

Winiarski

2020

IEEE Access

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Robots, in contrast to typical computational systems, affect the physical environment directly. Therefore, other assumptions must be considered for task management procedures in these system types. Robots coexist with humans in the environment and act upon potentially dangerous objects (e.g., a cooker); hence, extra safety procedures in robot task harmonisation must be ensured. Additionally, an algorithm that schedules tasks for a robot and optimises the robot's operation needs to consider the robot motion time, dynamics of the physical processes, changes in the robot user preferences and changes in the environment made by other habitants. In this article, we investigate the problem of switching between various independent tasks safely and state requirements for a control system resolving it. The tasks are uploaded to a store, launched on a robot at the user's request (similar to smartphone applications) and scheduled following a configurable algorithm. Furthermore, we design a model of systems satisfying the requirements. The systems are structured with agents of different classes. We propose a task-switching procedure and dedicated states of the finite-state machines describing the operation of the agents. Finally, we present a TaskER framework implementing the model, and we verify the model through the execution of an exemplary system in scenarios showing the benefits of the model implementation. As a result of our approach application, the robot tasks can be safely interrupted, postponed, resumed, and potential danger (e.g., leaving a cooker on for a long time) can be minimised.

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“…It is recognised that cognitive biases can influence the judgment of a designer (Stacy and MacMillan, 1995;Zalewski et al, 2017). For instance, a software designer can fixate or anchor on a solution that first came to his mind and as a result ignores other options.…”

Section: Reflection and Reasoning Remindersmentioning

confidence: 99%