89 lines
4.8 KiB
Markdown
89 lines
4.8 KiB
Markdown
type:: [[meeting]]
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external-links::
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tags:: [[PROJECTS/PRIN-EMELIOT]]
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people::
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date:: [[15-06-2023]]
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location:: Salerno
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isconsortium:: true
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- ### My Slides
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- https://docs.google.com/presentation/d/1nO3AzY2hLir93rUUXP5Ck06ixz37obloj_ZZJuE__LE/edit#slide=id.p1
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- ### Invited Talks
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- *Software Engineering for Machine Learning, some First Experiences (L. Baresi)*
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- **Data Scientist vs Software Engineering (Christian Kaenstner)**
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- SEs are concerned about cost, performance, stability, safety, security, and release time. DEs are not.
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- **Interesting papers**
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- [[2105.01984] Software Engineering for AI-Based Systems: A Survey (arxiv.org)](https://arxiv.org/abs/2105.01984)
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collapsed:: true
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- [[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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collapsed:: true
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- 
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- **Federated Machine Learning**
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- They approach the problem of configuring and use ML systems as a self-adapting problem
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- HYPERFL
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- Extension of TensorFlow they have developed
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- Main issues
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- Privacy
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- Start analyzing local and send to the cloud service the output of the analysis
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- Network ovrhead
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- **DeepNurse**
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- 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?
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- **Feature model + reinforcement learning for self-adaptive services**
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- *Machine Learning in Software Engineering: How the Software Engineering Lanscape Has Changed in the Last 20 Years (M. Di Penta)*
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- **Interesting papers**
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- [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)
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- [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)
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- **Notes**
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- Ensemble classifier
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- Creates multiple models each with different charactertics
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- THis is when a simple model does not work. Depending on the source model you decide which one performs better
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- Textual analysis to scource code. Andrea has been among the first to use such a kind of analysis.
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- [Recovering Traceability Links between Code and Documentation](https://ieeexplore-ieee-org.univaq.idm.oclc.org/document/1041053)
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- Recent paper from David Lo to detect Technical Dept
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- A recommender systems should be able to explain, but with deep learning this is difficult
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- Rise of deep learning applications to SE over the last 7 papers
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- Better hardware, etc.
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- Many data sources
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- World of Code - it's a recent data source
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- Better AI models
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- We have pre-trained models
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- ICSE2023 had a full session on this
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- [[LLMs]]
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- OUR ROLSE AS SE EXPERTS
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- SE for AI
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- Understanding
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- Refactoring
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- Testing (e.g. of DL models)
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- Search the slides of Paola Tonella who delivered an invited talk at ICSE
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- Debugging
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- Problem and data knowledge
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- Preprocess the data,
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- combine data from different sources,
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- pretrainend models might not be ok as they are for the problem at hand and thus need to be refined
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- Prompting in SE
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- [[2207.11680] No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence (arxiv.org)](https://arxiv.org/abs/2207.11680)
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- Process and context knowledge
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- System Engineering
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- Usefulness evaluation
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- Legal Issues
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- LIcenses regulate derivative work. Different licenses (more or less restrictive)
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- GPL is the most restrictive, the most permissibe are MIT, BSD, Apache.
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- There are discussios:
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- AI generative code is not derivative code
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- Software bill of materials
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- Similar to the list of ingredients used in a food product, or to the Bill o Material available for manufacturing products
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- AI-related challenges
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- Dimensions to investigate
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- Productvity
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- code correctenss
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- Code quality
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- ### Discussion about the experiments to be done in the project
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- Capire come gli sviluppatori beneficiano di strumenti tipo CoPilot, ChatGPT, etc
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- 2 unità che si concentrano sullo stesso task
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- A livello di field study le unità possono prendere diversi progettini di interesse
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- ### PREVIOUS MEETING IN PREPARATION OF THIS MEETING
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- {{embed ((64885a41-6eb1-4bee-a9eb-ede87be14999))}}
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