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A runtime framework for machine-augmented software design using unsupervised self-learning

Research output: Contribution to conference - Without ISBN/ISSN › Poster › peer-review

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A runtime framework for machine-augmented software design using unsupervised self-learning. / Rodrigues Filho, Roberto; Porter, Barry Francis.
2016. 231-232 Poster session presented at Autonomic Computing (ICAC), 2016 IEEE International Conference on.

Research output: Contribution to conference - Without ISBN/ISSN › Poster › peer-review

Harvard

Rodrigues Filho, R & Porter, BF 2016, 'A runtime framework for machine-augmented software design using unsupervised self-learning', Autonomic Computing (ICAC), 2016 IEEE International Conference on, 17/07/16 - 22/07/16 pp. 231-232. https://doi.org/10.1109/ICAC.2016.37

APA

Rodrigues Filho, R., & Porter, B. F. (2016). A runtime framework for machine-augmented software design using unsupervised self-learning. 231-232. Poster session presented at Autonomic Computing (ICAC), 2016 IEEE International Conference on. https://doi.org/10.1109/ICAC.2016.37

Vancouver

Rodrigues Filho R, Porter BF. A runtime framework for machine-augmented software design using unsupervised self-learning. 2016. Poster session presented at Autonomic Computing (ICAC), 2016 IEEE International Conference on. doi: 10.1109/ICAC.2016.37

Author

Rodrigues Filho, Roberto ; Porter, Barry Francis. / A runtime framework for machine-augmented software design using unsupervised self-learning. Poster session presented at Autonomic Computing (ICAC), 2016 IEEE International Conference on.2 p.

Bibtex

@conference{937f2e6072d646d29b3e6908b53cee91,
title = "A runtime framework for machine-augmented software design using unsupervised self-learning",
abstract = "Modern computer software comprises tens of millions of lines of code and is deployed in highly dynamic environments such as data-centres, with constantly fluctuating user populations and popular content patterns. Together this complexity and dynamism make computer software very difficult to develop and maintain. The autonomic computing community has grown to address some of these challenges, developing automation in areas such as self-optimisation and self-healing. However, work to date either (i) focuses on a specific problem in isolation, neglecting the broader complexity of software construction, or (ii) considers the design process but is human-centric, relying on expertly-crafted models. In this paper we examine software development as a process, infusing this process with a level of autonomy that seeks to make software an active part of its own development team. We present an overview of our framework and we demonstrate the accuracy of our framework in autonomously finding the most suitable software design at runtime according to specific operating conditions.",
author = "{Rodrigues Filho}, Roberto and Porter, {Barry Francis}",
year = "2016",
month = jul,
day = "20",
doi = "10.1109/ICAC.2016.37",
language = "English",
pages = "231--232",
note = "Autonomic Computing (ICAC), 2016 IEEE International Conference on ; Conference date: 17-07-2016 Through 22-07-2016",

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RIS

TY - CONF

T1 - A runtime framework for machine-augmented software design using unsupervised self-learning

AU - Rodrigues Filho, Roberto

AU - Porter, Barry Francis

PY - 2016/7/20

Y1 - 2016/7/20

N2 - Modern computer software comprises tens of millions of lines of code and is deployed in highly dynamic environments such as data-centres, with constantly fluctuating user populations and popular content patterns. Together this complexity and dynamism make computer software very difficult to develop and maintain. The autonomic computing community has grown to address some of these challenges, developing automation in areas such as self-optimisation and self-healing. However, work to date either (i) focuses on a specific problem in isolation, neglecting the broader complexity of software construction, or (ii) considers the design process but is human-centric, relying on expertly-crafted models. In this paper we examine software development as a process, infusing this process with a level of autonomy that seeks to make software an active part of its own development team. We present an overview of our framework and we demonstrate the accuracy of our framework in autonomously finding the most suitable software design at runtime according to specific operating conditions.

AB - Modern computer software comprises tens of millions of lines of code and is deployed in highly dynamic environments such as data-centres, with constantly fluctuating user populations and popular content patterns. Together this complexity and dynamism make computer software very difficult to develop and maintain. The autonomic computing community has grown to address some of these challenges, developing automation in areas such as self-optimisation and self-healing. However, work to date either (i) focuses on a specific problem in isolation, neglecting the broader complexity of software construction, or (ii) considers the design process but is human-centric, relying on expertly-crafted models. In this paper we examine software development as a process, infusing this process with a level of autonomy that seeks to make software an active part of its own development team. We present an overview of our framework and we demonstrate the accuracy of our framework in autonomously finding the most suitable software design at runtime according to specific operating conditions.

U2 - 10.1109/ICAC.2016.37

DO - 10.1109/ICAC.2016.37

M3 - Poster

SP - 231

EP - 232

T2 - Autonomic Computing (ICAC), 2016 IEEE International Conference on

Y2 - 17 July 2016 through 22 July 2016

ER -