Decision Making and Cognitive Biases in Designing Software Architectures

Manjunath, Akash; Bhat, M. Seetharama; Shumaiev, Klym; Biesdorf, Andreas; Matthes, Florian

doi:10.1109/icsa-c.2018.00022

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…For decades of architectural knowledge research [31], [32], it has been maintained that quality attributes and their metrics are critical during the decision-making process [33]. Software architects play an important role in the decision-making practice, but they are biased in the course of the process [34], [35]. This bias must be reduced as much as possible, since design decision-making is the main driver of software architecture construction [6].…”

Section: A Software Architecturesmentioning

confidence: 99%

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

2021

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The new era of information and the needs of our society require continuous change in software and technology. Changes are produced very quickly and software systems require evolving at the same velocity, which implies that the decision-making process of software architectures should be (semi-)automated to satisfy changing needs and to avoid wrong decisions. This issue is critical since suboptimal architecture design decisions may lead to high cost and poor software quality. Therefore, systematic and (semi-)automated mechanisms that help software architects during the decision-making process are required. Architectural patterns are one of the most important features of software applications, but the same pattern can be implemented in different ways, leaving to results of different quality. When an application requires to evolve, knowledge extracted from similar applications is useful for driving decisions, since quality pattern implementations can be reproduced in similar applications to improve specific quality attributes. Therefore, clustering methods are especially suitable for classifying similar pattern implementations. In this paper, we apply a novel unsupervised clustering technique, based on the well-known artificial neural network model Self-Organizing Maps, to classify Model-View-Controller (MVC) pattern from a quality point of view. Software quality is analyzed by 24 metrics organized into the categories of Count/Size, Maintainability, Duplications, Complexity, and Design Quality. The main goal of this work is twofold: to identify the quality features that establish the similarity of MVC applications without software architect bias, and to classify MVC applications by means of Self-Organizing Maps based on quality metrics. To that end, this work performs an exploratory study by conducting two analyses with a dataset of 87 Java MVC applications characterized by the 24 metrics and two attributes that describe the technology dimension of the application. The stated findings provide a knowledge base that can help in the decision-making process for the architecture of Java MVC applications.

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Section: A Software Architecturesmentioning

confidence: 99%

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

2021

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“…It is also important to characterise and contrast the decision-making models of the communities. Industrialists often follow a "naturalistic" decision-making approach [11] and the "satisficing" heuristic [12], in their demanding, real-world situations. These include situations marked by limited time, uncertainty, high stakes, team and organizational constraints, unstable conditions, and varying amounts of experience; conditions which are often the case in large-scale SE projects.…”

Section: Two Communities Different Mind-sets Different Objectivesmentioning

confidence: 99%

“…For instance, during our survey on when to automate testing [16], we found many such materials, e.g., in a presentation (goo.gl/QJNfes) entitled "Choosing what to automate", a practitioner shared concrete suggestions on the topic: "Good candidates for test automation are short or simple tests, many data combinations, When expected results are stable, and when tasks that are difficult to do manually". On the other hand, the AL in this area has proposed systematic approaches to the problem, and while these approaches could be useful, they will need effort to be tailored to and be applied in specific contexts [11]. In our discussions with several SE practitioners, we have seen that it is not always easy to convince them to apply such systematic techniques, which are often costly to apply.…”

Section: Comparing the Two Literature Typesmentioning

confidence: 99%

Gray Literature Versus Academic Literature in Software Engineering: A Call for Epistemological Analysis

Garousi

Rainer

2021

IEEE Softw.

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To learn about novel software engineering (SE) trends, where do you refer to? In order to document and disseminate their experience / knowledge, many SE practitioners prepare technical materials and share them online as blog posts, white papers and videos. Such materials are often called "grey literature" because they are not formally peer reviewed. By contrast, SE researchers write technical papers that are peer-reviewed and published as academic literature. We observe that, in general, these two communities mostly read literature that is only written by and published within their respective communities. This situation has led to a form of "knowledge divide" between the two communities that, we believe, hurts both communities. By characterizing and contrasting the two types of literature, grey and academic, we discuss how each literature can complement the other and can lead to a richer and more integrated knowledge exchange and dissemination in SE.

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“…As complexity grows, practices and methods need to evolve in parallel to manage the development of such systems. With the growth in these technologies, several solutions are becoming available in the context of developing complex CPSs, incorporating several technologies, such as Model‐Driven Engineering (MDE), 2 DevOps, 3 Artificial Intelligence/Machine Learning (AI/ML), 4 and many others.…”

Section: Introductionmentioning

confidence: 99%

Experiences and challenges from developing cyber‐physical systems in industry‐academia collaboration

Cederbladh,

Eramo,

Muttillo

et al. 2024

Softw Pract Exp

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SummaryCyber‐physical systems (CPSs) are increasing in developmental complexity. Several emerging technologies, such as Model‐based engineering, DevOps, and Artificial intelligence, are expected to alleviate the associated complexity by introducing more advanced capabilities. The AIDOaRt research project investigates how the aforementioned technologies can assist in developing complex CPSs in various industrial use cases. In this paper, we discuss the experiences of industry and academia collaborating to improve the development of complex CPSs through the experiences in the research project. In particular, the paper presents the results of two working groups that examined the challenges of developing complex CPSs from an industrial and academic perspective when considering the previously mentioned technologies. We present five identified challenge areas from developing complex CPSs and discuss them from the perspective of industry and academia: data, modeling, requirements engineering, continuous software and system engineering, as well as intelligence and automation. Furthermore, we highlight practical experience in collaboration from the project via two explicit use cases and connect them to the challenge areas. Finally, we discuss some lessons learned through the collaborations, which might foster future collaborative efforts.

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Decision Making and Cognitive Biases in Designing Software Architectures

Cited by 11 publications

References 8 publications

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

Gray Literature Versus Academic Literature in Software Engineering: A Call for Epistemological Analysis

Experiences and challenges from developing cyber‐physical systems in industry‐academia collaboration

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