type::  [[meeting]]
external-links::   
tags:: [[PROJECTS/PRIN-EMELIOT]]  
people:: 
date:: [[15-06-2023]] 
location:: Salerno
isconsortium:: true

	- ### My Slides
		- https://docs.google.com/presentation/d/1nO3AzY2hLir93rUUXP5Ck06ixz37obloj_ZZJuE__LE/edit#slide=id.p1
	- ### Invited Talks
		- *Software Engineering for Machine Learning, some First Experiences (L. Baresi)*
			- **Data Scientist vs Software Engineering (Christian Kaenstner)**
				- SEs are concerned about cost, performance, stability, safety, security, and release time. DEs are not.
			- **Interesting papers**
				- [[2105.01984] Software Engineering for AI-Based Systems: A Survey (arxiv.org)](https://arxiv.org/abs/2105.01984)
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					- ![image.png](../assets/image_1686813801082_0.png)
					- ![image.png](../assets/image_1686813885068_0.png)
				- [[2011.03751] Software engineering for artificial intelligence and machine learning software: A systematic literature review (arxiv.org)](https://arxiv.org/abs/2011.03751)
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					- ![image.png](../assets/image_1686814007762_0.png)
			- **Federated Machine Learning**
				- They approach the problem of configuring and use ML systems as a self-adapting problem
					- HYPERFL
						- Extension of TensorFlow they have developed
				- Main issues
					- Privacy
						- Start analyzing local and send to the cloud service the output of the analysis
					- Network ovrhead
			- **DeepNurse**
				- It's not clear how this works. How is it able to detect new domains? By replacing neural networks at run-time? WHat do you means with self-adapt neural networks? Changes them or refine the values of the hyoerparameters?
			- **Feature model + reinforcement learning for self-adaptive services**
			-
		- *Machine Learning in Software Engineering: How the Software Engineering Lanscape Has Changed in the Last 20 Years (M. Di Penta)*
			- **Interesting papers**
				- [A validation of object-oriented design metrics as quality indicators | IEEE Journals & Magazine | IEEE Xplore (oclc.org)](https://ieeexplore-ieee-org.univaq.idm.oclc.org/document/544352)
				- [A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction | IEEE Conference Publication | IEEE Xplore (oclc.org)](https://ieeexplore-ieee-org.univaq.idm.oclc.org/document/4814129)
			- **Notes**
				- Ensemble classifier
					- Creates multiple models each with different charactertics
						- THis is when a simple model does not work. Depending on the source model you decide which one performs better
					- Textual analysis to scource code. Andrea has been among the first to use such a kind of analysis.
						- [Recovering Traceability Links between Code and Documentation](https://ieeexplore-ieee-org.univaq.idm.oclc.org/document/1041053)
				- Recent paper from David Lo to detect Technical Dept
				- A recommender systems should be able to explain, but with deep learning this is difficult
				- Rise of deep learning applications to SE over the last 7 papers
					- Better hardware, etc.
					- Many data sources
						- World of Code - it's a recent data source
					- Better AI models
					- We have pre-trained models
						- ICSE2023 had a full session on this
					- [[LLMs]]
				- OUR ROLSE AS SE EXPERTS
					- SE for AI
						- Understanding
						- Refactoring
						- Testing (e.g. of DL models)
							- Search the slides of Paola Tonella who delivered an invited talk at ICSE
						- Debugging
					- Problem and data knowledge
						- Preprocess the data,
						- combine data from different sources,
						- pretrainend models might not be ok as they are for the problem at hand and thus need to be refined
							- Prompting in SE
								- [[2207.11680] No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence (arxiv.org)](https://arxiv.org/abs/2207.11680)
					- Process and context knowledge
					- System Engineering
					- Usefulness evaluation
					- Legal Issues
						- LIcenses regulate derivative work. Different licenses (more or less restrictive)
							- GPL is the most restrictive, the most permissibe are MIT, BSD, Apache.
						- There are discussios:
							- AI generative code is not derivative code
						- Software bill of materials
							- Similar to the list of ingredients used in a food product, or to the Bill o Material available for manufacturing products
							- AI-related challenges
					- Dimensions to investigate
						- Productvity
						- code correctenss
						- Code quality
	- ### Discussion about the experiments to be done in the project
		- Capire come gli sviluppatori beneficiano  di strumenti tipo CoPilot, ChatGPT, etc
			- 2 unità che si concentrano sullo stesso task
			- A livello di field study le unità possono prendere diversi progettini di interesse
	- ### PREVIOUS MEETING IN PREPARATION OF THIS MEETING
		- {{embed ((64885a41-6eb1-4bee-a9eb-ede87be14999))}}
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