(Don't read this page. It is a work in progress for a Fall'19 graduate automated SE subject at NC State. Come back in mid-October!)

Start

1.preface
2.why se 4 ai?
3.tools
4.ethics: how

Tools

baselines
Data mining:
discretization
basic
advanced
Optimizers:
landscapes
basic
advanced
optimizing+data mining
Theorem provers:
basic
advanced

Process

requirements
collect
cleanse
label
train
eval
deploy
monitor

Code

config
tests

Exercises

1
2
3a
3b
3c
3d
4

1.Preface

Ethically-aligned Design
Industrial Focus
Data Miners + Optimizers + Theorem Provers
Note to Educators

This book is about using better software engineering to build better AI software. AI is a very broad topic, discussed in so very many other books. So what makes this book so different?

Ethically-aligned Design

Firstly, by “better” AI software, we mean “ethically-aligned”. We assert that the design goals for SE-for-AI must be prioritize for human well-being.

It is ethical to improve the profits of your company since that money becomes wages which becomes groceries which becomes dinner so everyone and their kids can sleep better at night.
It is also ethical to change the design of AI software in order to ensure that (say) the software is not unduly discriminatory towards a particular social group (e.g. some groups characterized by age, race, or gender).

As discussed below, AI tools offer us more choices. This means that they also offer us more ethical choices. Better yet, AI also gives us methods for automatically tracking down what choices are useful we want within a very large space of possibilities. That is, once we tell AI that we want our systems to achieve ethical goals then:

AI tools lets us be more ethical than ever before.
But only if we first accept the merits and methods of an ethical approach.

So we suggest to you that when you start a new AI project:

Your first question should not be (e.g.) “what data miners should I apply to this data?”.
Rather, it should be “what are the ethical requirements of this development? And how can we best support those requirements?”.

Industrial Focus

The second way this book is unique is that the authors of this book have spent decades applying many AI tools (mostly for software engineering applications). We share some of that case study experience in this book. For example, recently we reversed engineered from multiple AI applications a nine-step industrial AI pipeline. This book devoted nine chapters to that pipeline:

Data Miners + Optimizers + Theorem Provers

Thirdly, we look at AI tools which, recently, have had a major impact on software engineering. Specifically, we will talk much about data mining algorithms; some about optimizers; and a little about theorem provers. When combined, these AI tools are a rich tapestry within which software engineers can weave around to achieve a variety of goals.

Optimizers can better control the data miners. Several research has improved data mining performance via optimizers that tune the control parameters of the data miner (See Agrawal 2018a, Fu’18, and Tantithamthavorn et al.).
Data miners can better control the optimizers. For example, Majumder et al. used k-means clustering to divide up a complex text mining problem, then apply optimizers within each cluster. They report that this method speeds up their processing by up to three orders of magnitude.
Data miners can act as optimizers. For example, Chen et al. show that their recursive descent bi-clustering algorithm (which is a data mining technique) out-performs traditional evolutionary algorithms for the purposes of optimizing SE models.
Data miners and optimizers can be mashed up to (say) explore complex problems where one defines interesting regions where the other can reason faster, and better. For example, Abdessalem et al. generate test cases for autonomous cars via a cyclic approach where an optimizer reflects on the output of data miners that reflect on the output of an optimizer (and so on).
Theorem provers can be data generators for data miners or optimizers. When models come with many constraints, we can use theorem provers to generate valid examples;
Optimizers can improve theorem provers: Theorem provers deliver solutions in an order dictated by their internal design. This means, when there are very many ways to solve constraints, theorem provers can take a while to generate solutions that we prefer. In this case, a useful trick is to first , run theorem provers (a little) to get a sample of solutions; then second run mutators and optimizers to combine that sample in interesting ways.

Note to Educators

Please enjoy this book. If you are teaching this material at the graduate level, note that in our table of contents (shown at top-of-page) there is an exercises section listing some weekly homeworks and some idea for month-long projects.

Have fun! Be more ethical!

Tim Menzies (timm)
Tom Zimmermann (zimm)
August, 2019