Reusability in Artificial Neural Networks

Ghofrani, Javad; Kozegar, Ehsan; Soorati, Mohammad Divband

doi:10.1145/3307630.3342419

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…The other two from the same authors [196,197] target the derivation of testing frameworks for evaluating properties of an autonomous driving system with ML components. Lastly, we identified three primary studies addressing model reuse using different approaches, such as an analysis of current model reuse practices and challenges for building systems based on artificial neural networks [67],…”

Section: Software Engineering Models and Methods (38 Studies)mentioning

confidence: 99%

Software Engineering for AI-Based Systems: A Survey

Martínez-Fernández,

Bogner,

Franch

et al. 2021

Preprint

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AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-and speechrecognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems.To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula. CCS Concepts: • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning;

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Section: Software Engineering Models and Methods (38 Studies)mentioning

confidence: 99%

Software Engineering for AI-Based Systems: A Survey

Martínez-Fernández,

Bogner,

Franch

et al. 2021

Preprint

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“…Considering the reverse direction of applying variability concepts to machine learning, Ghofrani et al [54] propose to investigate product lines of deep neural networks, which establish reuse of existing trained networks by identifying features and composing them. They investigate the reuse potential in an associated empirical study [55]. Feature metrics.…”

Section: Background and Related Workmentioning

confidence: 99%

Feature-Oriented Defect Prediction: Scenarios, Metrics, and Classifiers

Mukelabai,

Strüder,

Strüber

et al. 2021

Preprint

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Software defects are a major nuisance in software development and can lead to considerable financial losses or reputation damage for companies. To this end, a large number of techniques for predicting software defects, largely based on machine learning methods, has been developed over the past decades. These techniques usually rely on code-structure and process metrics to predict defects at the granularity of typical software assets, such as subsystems, components, and files. In this paper, we systematically investigate feature-oriented defect prediction: predicting defects at the granularity of features-domain-entities that abstractly represent software functionality and often cross-cut software assets. Feature-oriented prediction can be beneficial, since: (i) particular features might be more error-prone than others, (ii) characteristics of features known as defective might be useful to predict other error-prone features, and (iii) feature-specific code might be especially prone to faults arising from feature interactions. We explore the feasibility and solution space for feature-oriented defect prediction. We design and investigate scenarios, metrics, and classifiers. Our study relies on 12 software projects from which we analyzed 13,685 bug-introducing and corrective commits, and systematically generated 62,868 training and test datasets to evaluate the designed classifiers, metrics, and scenarios. The datasets were generated based on the 13,685 commits, 81 releases, and 24, 532 permutations of our 12 projects depending on the scenario addressed. We covered scenarios, such as just-in-time (JIT) and cross-project defect prediction. Our results confirm the feasibility of feature-oriented defect prediction. We found the best performance (i.e., precision and robustness) when using the Random Forest classifier, with process and structure metrics. Surprisingly, we found high performance for single-project JIT (median AUROC ≥ 95 %) and release-level (median AUROC ≥ 90 %) defect prediction-contrary to studies that assert poor performance due to insufficient training data. Lastly, we found that a model trained on release-level data from one of the twelve projects could predict defect-proneness of features in the other eleven projects with median performance of 82 %, without retraining on the target projects. Our results suggest potential for defect-prediction model-reuse across projects, as well as more reliable defect predictions for developers as they modify or release software features.

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“…Second category provides computing power as processing services or data-storage service to handle the complexity of training ANNs. Microsoft Azure Machine Learning Studio 6 To end this section a comparison of RAN2 with other existing tools in this area will be provided. Tools RAN2 is competing with and where we're going to focus on are Google Colab, But4Reuse and OpenML.…”

Section: Related Workmentioning

confidence: 99%

“…2) improving the ranking system with textual reviews or comments, (3) enabling users to add additional information-meta data, description and documentation-to improve flexibility, transparency, and reusability, and (4) providing a machine-to-machine interface for automated cooperation between systems to automatically search and find a solution and reuse it without any need for human interference. Other future work that is necessary is (5) how the moderation of the feedback feature should be realized and (6) what should be considered when the search feature is implemented. Other issues are (7) what happens when the origin of a cloned project is updated and (8) a further analysis of the needs of the end-user.…”

Section: Issues In Generalmentioning

confidence: 99%

“…Model based software development based on reuse is well studied, however, less attention has been made to applying concept of reuse in the development of ANN based systems. Reusing artifacts of ANN based solutions is limited to models while data (test and training data) and code are less considered [6]. To a prior submission of this paper we received a lot of feedback concerning thoughts of additional features, issues others saw with the idea, points that needed to be explained in more detail as well as what makes our idea different from that of others.…”

Section: Introductionmentioning

confidence: 99%

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Repository for Reusing Artifacts of Artificial Neural Networks

Ghofrani¹,

Kozegar²,

Soorati³

et al. 2020

Preprint

Self Cite

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Artificial Neural Networks (ANNs) replaced conventional software systems in various domains such as machine translation, natural language processing, and image processing. So, why do we need an repository for artificial neural networks? Those systems are developed with labeled data and we have strong dependencies between the data that is used for training and testing our network. Another challenge is the data quality as well as reuse-ability. There we are trying to apply concepts from classic software engineering that is not limited to the model, while data and code haven't been dealt with mostly in other projects. The first question that comes to mind might be, why don't we use GitHub, a well known widely spread tool for reuse, for our issue. And the reason why is that GitHub, although very good in its class is not developed for machine learning appliances and focuses more on software reuse. In addition to that GitHub does not allow to execute the code directly on the platform which would be very convenient for collaborative work on one project. We propose an online single-purpose repository of artifacts used during the development of ANNs as a cooperation platform for machine learning experts. Users can search, share, or reuse an existing artifact and submit their reviews to promote high value artifacts. Given the costs of developing ANNs-large datasets and high computational power-our tool facilitates reusability and

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Reusability in Artificial Neural Networks

Cited by 10 publications

References 18 publications

Software Engineering for AI-Based Systems: A Survey

Software Engineering for AI-Based Systems: A Survey

Feature-Oriented Defect Prediction: Scenarios, Metrics, and Classifiers

Repository for Reusing Artifacts of Artificial Neural Networks

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