acmsigsoft / www.sigsoft.org Goto Github PK

Dietmar with Jacopo as the SEN editor on the pages below.

• SEN Webpage: https://www.sigsoft.org/SEN/editors.html

• SIGSOFT info pages: https://www.sigsoft.org/execcomm.html and https://www.sigsoft.org/contact.html

In particular, could you kindly tell us who should we contact for this?

Hello,

I'm the SIGSOFT CAPS coordinator and would like to request the update for the https://awards.sigsoft.org/CAPS/ page since we have been using other channels to manage CAPS for the past couple of years. My request is to keep the 'SIGSOFT CAPS Application' at the top and to remove everything else currently on the page. We'll then provide the link and important dates to specific CAPS for different conferences, e.g., MODELS'22, ASE'22, and ESEC/FSE'22.

These links will be created in the next few days & I'll keep everybody posted.

Thanks,
Nan

Starting the process to add De Sanctis

raise this page for the ISSTA Impact paper awards.

As content for the award, please use this page: https://www.doc.ic.ac.uk/~cristic/issta-awards.html

1. Your first name:
   Karthik

2. Your last name:
   Vaidhyanthan

3. Institution that granted the PhD:
   Gran Sasso Science Institute

4. Date the PhD was granted (MM/DD/YYYY):
   01/27/2021

5. Your advisor's name:
   Henry Muccini

6. Title of your dissertation
   Data-Driven Self-Adaptive Architecting Using Machine Learning

7. Abstract:
   The last decade has seen a significant evolution in software architecting practices as the process of managing and developing software is becoming more and more complex. This is especially true due to the heterogeneous composition of modern software systems
   coupled with the run-time uncertainties. These include application downtime due to high CPU utilization, server outages, resource constraints, dynamic resource demands, etc. These can have a big impact on the Quality of Service (QoS) oered by the system,
   thereby impacting the experience of the end-user. Self-adaptation is nowadays considered to be one of the best solutions to dynamically reconfigure a system in the occurrence of deviations from the expected QoS. However, one of the issues with the existing solutions is that most of them are reactive in nature, where adaptation is carried out in the event of uncertainties. Moreover, current adaptation methods: i) do not provide the systems the ability to proactively identify the need for adaptation with good accuracy; ii) may temporally overcome an impending failure, while not preventing the system from the state in the future. In essence, they do not exploit the knowledge gained from every adaptation performed. The use of machine learning techniques to aid self-adaptation has been proposed in the literature as a way to mitigate this problem based on the concept of self-adaptation through achieving, but not much work has been done in this area. Moreover, the challenges associated with learning bias and less accurate predictions also need to be handled while using machine learning techniques, which otherwise leads to sub-optimal adaptations. To this end, in this thesis, we develop a data-driven approach to architecting self-adaptive systems using machine learning techniques. The approach, in principle, shifts the focus from self-adaptive architectures to self-learning architectures. It achieves this by using a combination of deep neural networks and reinforcement learning (RL) techniques to ensure that the system continuously learns and improves the quality of adaptation performed over time. It further uses quantitative verification (QV) techniques to overcome learning bias and enable faster convergence towards optimal adaptations. More specifically the approach i) continuously monitors the system data; ii) uses deep neural networks to forecast any QoS uncertainties; iii) leverages the forecasts using RL techniques to identify the adaptation strategy; iv) it further uses QV techniques to verify the decision selected by RL; v) keeps improving the decisions based on the forecasts as well as the feedbacks obtained from QV; vi) continuously keeps performing the loop of learning, selection, verification, and adaptations to converge towards optimal adaptations, thereby enabling the architectures to learn and improve over time.

8. URL of your full dissertation (or email address from which to obtain a copy):
   https://iris.gssi.it/handle/20.500.12571/15976

9. URL of your home page:
   https://karthikvaidhyanathan.com

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Hello @raux and @tzimmermsr

I'm not sure if I've raised this issue before, but I'd like to request a folder structure to upload the CAPS reports written by awardees (mostly, students). I'd like to link these reports to the page https://www.sigsoft.org/resources/caps.html For example, 'ASE 2022' received two students' reports in PDF format. I'd like to be able to upload these PDF reports to a folder (e.g., named CAPS_Reports/ASE_2022) and then published them on the page.

Can you help?

Thanks,
Nan