Identifying modularization patterns by visual comparison of multiple hierarchies

Beck, Fabian; Melcher, Jan; Weiskopf, Daniel

doi:10.1109/icpc.2016.7503712

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…Among the limited existing approaches enabling comparison of multiple hierarchies, juxtaposition is commonly used instead of a consolidated, single representation of a tree structure like in our approach. For instance, BarcodeTree [LZD*20] juxtaposes the condensed bar code tree representations, Beck et al [BMW16] use a juxtaposed icicle plot representation of hierarchies and TreeJuxtaposer [MGT*03] is a focus+context approach to compare juxtaposed trees represented in a dendrogram layout. Another general difference of our approach to these tree comparison methods is that we can also compare event-specific categories (i.e.…”

Section: Visual Tree and Stream Comparisonmentioning

confidence: 99%

“…For instance, BarcodeTree [LZD*20] juxtaposes the condensed bar code tree representations, Beck et al. [BMW16] use a juxtaposed icicle plot representation of hierarchies and TreeJuxtaposer [MGT*03] is a focus+context approach to compare juxtaposed trees represented in a dendrogram layout. Another general difference of our approach to these tree comparison methods is that we can also compare event‐specific categories ( i.e .…”

Section: Related Workmentioning

confidence: 99%

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Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison

Krause

Agarwal

Burch

et al. 2023

Computer Graphics Forum

Self Cite

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Event sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position double trees as a domain‐agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour‐coded categories. We integrate the double tree and category‐based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non‐spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach.

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Section: Visual Tree and Stream Comparisonmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison

Krause

Agarwal

Burch

et al. 2023

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

show abstract

“…AdaptiviTree [TSLR07] encoded real‐time game status information and indicated changes by color. Other work [BMW16, BvH∗11] used visual cues to allow viewers to compare different trees that were shown horizontally, with color‐coded similarity metrics based on juxtaposed icicle plots. Because we use the trees proposed by ML only as an initial guess and there is no ground truth, we do not show multiple trees and simply rely on color coding to encode differences.…”

Section: Related Workmentioning

confidence: 99%

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Hong

Trubuil

Isenberg

2022

Computer Graphics Forum

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We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.

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Automated construction of reference model for software remodularization through software evolution

Meng,

Yu,

Chong

et al. 2024

J Software Evolu Process

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The undocumented evolution of a software project and its underlying architecture underscores the need to recover the architecture from the software's implementation‐level artifacts. Despite the existence of various software remodularization techniques, they often suffer from inaccuracies, and evaluating their effectiveness is challenging due to the absence of accurate “ground‐truth” architectures or reference models. Prior studies on reference model construction are time‐consuming and labor‐intensive as it heavily relies on manual analysis by domain experts. Besides, other existing approaches that directly utilize the directory or package structure of the latest version can be unreliable, lacking in‐depth analysis of the employed software structure. To address the above limitations, in this paper, we propose Automated Construction of Reference Model (ACRM), an approach for automatically constructing reference models by assigning weights to classes for various software projects using the metadata of all software versions and historical maintenance records. We evaluate ACRM through both quantitative and qualitative analyses. The experiment results provide quantitative validation and show that the generated reference models are reasonable, as confirmed by the relationship between proposed reference models and architectural smells or bugs. Furthermore, we conduct a survey among the practitioners from industry, to gain insights from practitioners' practices and further validate the generated reference models. The survey shows that, on average, 87% of the participants agree with the reference models generated by ACRM. Moreover, we propose an improved metric, wc2c, which analyzes the strengths and weaknesses of different types of software clustering techniques using the proposed reference models of the given software. Finally, we discuss the potential benefits of using ACRM in analyzed projects, particularly in terms of improving software quality, reducing maintenance costs, and enhancing developer productivity.

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Identifying modularization patterns by visual comparison of multiple hierarchies

Cited by 5 publications

References 38 publications

Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison

Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Automated construction of reference model for software remodularization through software evolution

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