ADeX: A Tool for Automatic Curation of Design Decision Knowledge for Architectural Decision Recommendations

Bhat, M. Seetharama; Tinnes, Christof; Shumaiev, Klym; Biesdorf, Andreas; Hohenstein, Uwe; Matthes, Florian

doi:10.1109/icsa-c.2019.00035

Cited by 17 publications

(5 citation statements)

References 13 publications

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“…With regard to DD, the work of Bhat et al [77] identifies DD from the issue management systems that manage the software architectures. This work has improved by Bhat et al [78] supporting the DD-making process in terms of quality criteria and clustering the DD using the k-means algorithm.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

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

2021

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“…Bhat et al proposed a two-phase supervised machine learning based approach to automatically detect design decisions from issues in issue tracking systems of two large OSS projects, and then classify the detected design decisions into three categories (i.e., Behavioral decision, Structural decision, and Ban decision) [4]. In their follow-up work, they developed a tool named ADeX to automatically extract, enrich, and generate specific views on architectural knowledge to support architects' decisions-making process [5]. Shahbazian et al developed a technique called RecovAr to automatically recover design decisions from the readily available history artifacts of projects (e.g., an issue tracker and version control repository) [34].…”

Section: Related Work 21 Decisions In Software Developmentmentioning

confidence: 99%

“…Inspired by the work above (i.e., [4,5,19,34]), we are motivated to develop an automatic approach to help researchers and practitioners better understand specific types of decisions in software development. The difference between our work and existing work is that we considered classifying decisions into five decision types that are beneficial to various types of stakeholders in software development.…”

Section: Related Work 21 Decisions In Software Developmentmentioning

confidence: 99%

A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions

Liang

et al. 2021

Evaluation and Assessment in Software Engineering

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Stakeholders make various types of decisions with respect to requirements, design, management, and so on during the software development life cycle. Nevertheless, these decisions are typically not well documented and classified due to limited human resources, time, and budget. To this end, automatic approaches provide a promising way. In this paper, we aimed at automatically classifying decisions into five types to help stakeholders better document and understand decisions. First, we collected a dataset from the Hibernate developer mailing list. We then experimented and evaluated 270 configurations regarding feature selection, feature extraction techniques, and machine learning classifiers to seek the best configuration for classifying decisions. Especially, we applied an ensemble learning method and constructed ensemble classifiers to compare the performance between ensemble classifiers and base classifiers. Our experiment results show that (1) feature selection can decently improve the classification results; (2) ensemble classifiers can outperform base classifiers provided that ensemble classifiers are well constructed; (3) BoW + 50% features selected by feature selection with an ensemble classifier that combines Naïve Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) achieves the best classification result (with a weighted precision of 0.750, a weighted recall of 0.739, and a weighted F1-score of 0.727) among all the configurations. Our work can benefit various types of stakeholders in software development through providing an automatic approach for effectively classifying decisions into specific types that are relevant to their interests.

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“…However, large datasets like DeepMind Kinetics 601 , ImageNet 564 , and YouTube 8M 602 may take a team months to prepare. As a result, it may not be practical to divert sufficient staff and resources to curate a high-quality dataset, even if curation is partially automated [602][603][604][605][606][607][608][609] . To curate data, human capital can be temporarily and cheaply increased by using microjob services 610 .…”

Section: Exit Wavefunction Reconstructionmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

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ADeX: A Tool for Automatic Curation of Design Decision Knowledge for Architectural Decision Recommendations

Cited by 17 publications

References 13 publications

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

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

A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions

Review: Deep Learning in Electron Microscopy

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