Vloeberghs Chair Lectures 21/22

Inaugural Vloeberghs Chair Lecture

Ralf Lämmel

Tuesday 17/05 16.00-18.00 in room I.2.03, followed by a reception

Title: Software knowledge analytics as a role model for making sense of the world

Abstract:

Humans, by average, are notoriously untalented when it comes to handling non-trivial situations that require aspects of logical and probabilistic reasoning. That is, humans indulge confirmation bias and ad-hoc reasoning; also, humans fall for “propaganda” and they “don’t look up”.

In this lecture (series), I advertise the non-empty intersection of software reverse engineers and knowledge engineers; these are humans, too, but they leverage a grown-up scientific method that largely neutralizes a lot of threats to validity otherwise implied by Twitter/Facebook style of “naturally intelligent” discourse. Here are the key characteristics of the method:

Data extraction and analysis: Once you have identified the artifacts that could inform your reasoning, then semi-automatically and continuously extract and analyze the info available, subject to a robust empirical research methodology.
Mathematical models: Once your research hypotheses suggest some variables of your world to follow a certain distribution, time series, ML model, or yet another type of mathematical model, then determine, simulate, and verify those models.
Logical reasoning: Once your research domain suggests any absolute or probabilistic “if-then relationships” about your data, hypotheses, and models, then capture and connect them to reasoning + explainability for your conclusions.
Semantic metadata: Once you can clarify the meaning (the scope) of your data, hypotheses, and models beyond a basic schema (e.g., to support provenance or durations of validity), then add such metadata and include it into all workflows.
Regression testing: Whatever you assumed and proved to be true at some point in time, based on some variables understood, some datasets included, subject to whatever reasoning, make sure your conclusions remain valid, as time advances and variables, datasets, and reasoning evolve.

In this lecture (series), I will demonstrate this method. For what it matters, I am working on a related textbook on “software knowledge analytics” – this will be a textbook on applying data science, data engineering as well as reasoning to the software area. The inaugural lecture will motivate the aforementioned method in general terms and it will cherry-pick from the “exercises” suggested by the “technical Vloeberghs Chair Lectures” – without requiring much background in computer science or software engineering.

Technical Vloeberghs Chair Lectures

Around 5 lectures from the following list will be covered properly; the rest in passing.

API clustering – An exercise in abstraction
Joint API usage – An exercise in causality
Graph language proliferation – An exercise in (language) usage analysis
Knowledge graph validation – An exercise in reasoning (with contexts & metadata)
Classifier discovery on Wikipedia – An exercise in ML-based knowledge engineering
Developer workflow modeling – An exercise in process mining
Linguistic architecture recovery – An exercise in rule-based reasoning
Simulation of MSR/ESE studies – An exercise in debugging threats to validity
Regression analysis of defect data – An exercise in multilevel modeling
API developer profiles – An exercise in hypothesis building and validation

Lecture times and rooms

Tuesday 17/05 16.00-18.00 in room I.2.03 (200 seats)
Wednesday 18/05 16.00-18.00. in room E.0.07 (64 seats)
Thursday 19/05 10:00-12:00. in room F4.11 (63 seats)
Friday 20/05 10:00-12:00. in room E.0.06 (62 places)

Acknowledgements

I (Ralf Lämmel) kindly acknowledge collaborations with and contributions by PhD students of the SoftLang Lab – even more importantly so, as I was away during my “Facebook vacation” and largely unavailable due to my “dean appointment” lately. The work in the lecture series is backed up by these persons I am fortunate to collaborate with – in alphabetical order (of last name):

Hakan Aksu:
- Working on developer expertise/profiles based on API footprint.
- Submitting thesis at the end of 2022.
- Contributing to these lectures:
  - API developer profiles
  - API clustering
  - Joint API usage
Johannes Härtel:
- Working on mathematical models in the ESE/MSR context.
- Submitting thesis in May +/- 2022.
- Contributing to these lectures:
  - API clustering
  - Linguistic architecture recovery
  - Simulation of MSR/ESE studies
  - Regression analysis of defect data
  - API clustering
  - Graph language proliferation
Marcel Heinz:
- Worked on data extraction/analysis/reasoning in the SE context.
- Defended last fall.
- Contributing to these lectures:
  - Knowledge discovery on Wikipedia
  - Linguistic architecture recovery
Philipp Seifer:
- Working on semantic (meta)data.
- Submitting thesis before the end of 2022.
- Contributing to these lectures:
  - Knowledge graph validation
  - Graph language proliferation
  - Developer workflow modeling

About Lecturer

Ralf Lämmel has been Professor of Computer Science at the University of Koblenz-Landau in Germany since 2007. In the past, he had held positions at Facebook London, the University of l'Aquila, Microsoft, USA, the Free University of Amsterdam, CWI (Dutch Center for Mathematics and Computer Science), and the University of Rostock, Germany.

His research and teaching interests include software/data engineering, software reverse engineering, software re-engineering, software language engineering, mining software repositories, program comprehension, functional programming, grammar-based and model-based techniques, and megamodeling. In his more recent work at Facebook, he applied machine learning (in a broad sense) in an infrastructural context while developing an increasing interest in data engineering and science. Areas of application concern ownership management, infrastructure simulation, and developer workflow analysis.

Ralf Lämmel is one of the founding fathers of the international summer school series on Generative and Transformational Techniques on Software Engineering (GTTSE) and the international conference on Software Language Engineering (SLE). He is the author of the award-winning Springer textbook on Software Language Engineering: Software Languages: Syntax, Semantics, and Metaprogramming, Springer, 2018. (The book received the Choice Award "Outstanding Academic Title" in 2019.)

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