AI Engineering: Software Engineering for Artificial Intelligence—Development of Complex Machine Learning Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2021) | Viewed by 6003

Special Issue Editor

Department of Computer Science and Engineering, Chalmers University of Technology, Chalmersplatsen 4, 412 96 Göteborg, Sweden
Interests: software engineering; software architecture

Special Issue Information

Dear Colleagues,

In the development and implementation of advanced and complex AI applications, particularly in machine learning (ML) applications, the main challenge is not to develop the best models/algorithms but to provide support for the entire application lifecycle—from a business idea, through the collection and management of data, building AI algorithms, software development managing both data and code, product deployment and operation, and to its evolution. Software engineering has, since its initiation more than 50 years ago, provided many means of developing complex software systems. Some of these means can be utilized in the development of AI applications, but there are many new elements that require novel approaches to implement full support of software engineering for AI.

The main objective of this Special Issue is to identify challenges and needs, discuss experiences in the development of complex AI applications and AI-enabled systems, and consider the new approaches, theories, methods, and tools in software engineering that support this development.

Prof. Dr. Ivica Crnkovic
Guest Editor

Manuscript Submission Information

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Keywords

  • ML modeling and software system requirements
  • system and software architecture of AI-enabled systems
  • ML development process and software development processes
  • data management in AI system development
  • data security and privacy in AI system development
  • continuous deployment of AI-enabled systems
  • maintainability and evolution of AI-enabled systems
  • ML workflow and agile development
  • ML and software non-functional properties
  • AI system development teams
  • infrastructure engineering
  • validation of AI-enabled systems
  • best practices of development of AI-enabled systems
  • domain-specific AI application development
  • reuse of data and AI models

Published Papers (3 papers)

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Research

18 pages, 6310 KiB  
Article
Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
by Andreas Rausch, Azarmidokht Motamedi Sedeh and Meng Zhang
Appl. Sci. 2021, 11(21), 9881; https://0-doi-org.brum.beds.ac.uk/10.3390/app11219881 - 22 Oct 2021
Cited by 4 | Viewed by 1496
Abstract
Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. [...] Read more.
Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. Thus, novelty detection, that is, identifying data that differ in some respect from the data used for training, becomes a safety measure for system development and operation. In this study, we propose a new architecture for autoencoder-based semantic novelty detection with two innovations: architectural guidelines for a semantic autoencoder topology and a semantic error calculation as novelty criteria. We demonstrate that such a semantic novelty detection outperforms autoencoder-based novelty detection approaches known from the literature by minimizing false negatives. Full article
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14 pages, 3605 KiB  
Article
RT-SPeeDet: Real-Time IP–CNN-Based Small Pit Defect Detection for Automatic Film Manufacturing Inspection
by Geunwoo Ban and Joonhyuk Yoo
Appl. Sci. 2021, 11(20), 9632; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209632 - 15 Oct 2021
Cited by 4 | Viewed by 1639
Abstract
Pits are defects that occur during the film manufacturing process; they appear in the micrometer scale, which makes distinguishing them with the human eye difficult. Existing defect detectors have poor recognition rates for small objects or require a considerable amount of time. To [...] Read more.
Pits are defects that occur during the film manufacturing process; they appear in the micrometer scale, which makes distinguishing them with the human eye difficult. Existing defect detectors have poor recognition rates for small objects or require a considerable amount of time. To resolve these problems, we propose a real-time small pit defect detector (RT-SPeeDet), a two-stage detection model based on an image processing and convolutional neural network (IP–CNN) approach. The proposed method predicts boundary boxes using a lightweight image-processing algorithm optimized for pit defects, and applies binary classification to the predicted regions; thus, simultaneously simplifying the problem and achieving real-time processing speed, unlike existing detection methods that rely on CNN-based detectors for both boundary box prediction and classification. RT-SPeeDet uses lightweight image processing operations to extract pit defect candidate region image patches from high-resolution images. These patches are then passed through a CNN-based binary classifier to detect small pit defects at a real-time processing speed of less than 0.5 s. In addition, we propose a multiple feature map synthesis method that enhances the features of pit defects, enabling efficient detection of faint pit defects, which are particularly difficult to detect. Full article
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17 pages, 915 KiB  
Article
Improved Surprise Adequacy Tools for Corner Case Data Description and Detection
by Tinghui Ouyang, Vicent Sanz Marco, Yoshinao Isobe, Hideki Asoh, Yutaka Oiwa and Yoshiki Seo
Appl. Sci. 2021, 11(15), 6826; https://0-doi-org.brum.beds.ac.uk/10.3390/app11156826 - 25 Jul 2021
Cited by 5 | Viewed by 1590
Abstract
Facing the increasing quantity of AI models applications, especially in life- and property-related fields, it is crucial for designers to construct safety- and security-critical systems. As a major factor affecting the safety of AI models, corner case data and its related description/detection techniques [...] Read more.
Facing the increasing quantity of AI models applications, especially in life- and property-related fields, it is crucial for designers to construct safety- and security-critical systems. As a major factor affecting the safety of AI models, corner case data and its related description/detection techniques are important in the AI design phase and quality assurance. In this paper, inspired by surprise adequacy (SA), a tool having advantages on capture data behaviors, we developed three modified versions of distance-based-SA (DSA) for detecting corner cases in classification problems. Through the experiment analysis on MNIST, CIFAR, and industrial example data, the feasibility and usefulness of the proposed tools on corner case data detection are verified. Moreover, Qualitative and quantitative experiments validated that the developed DSA tools can achieve improved performance in describing corner cases’ behaviors. Full article
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