You begin to do software engineering research.
You want to have a research question.
You have several ideas but you wonder whether they are good enough for conducting a research.
1. Your question should contain well-defined terms.
Are you talking about something that everyone mostly agree about its definition and core characteristics.
For example, are you talking about Microservices, Event Sourcing, Relational Database, NoSQL, Unit Tests, Go Language, Speech Recognition, Speech Synthesis?
2. Your question should have a purpose and specific audience.
Why should the audience be interested in your question?
For example, are they going to upgrade a an event sourcing system? Are they going to apply test automation in our project?
Do they have specific security issues with their system?
Have they gotten specific performance issues with their system?
Are they going to build a new identity management platform for their legacy system?
Do they need to accelerate the development of a portal for their legacy system?
Are they going to integrate voice search into their existing system?
3. Your question should have verifiable answer.
What are the possible answers to your question? How can we compare these answers.
What is your concrete answer?
How can we replicate your answer?
How can we test your answer against the existing “standards“.
Why do I need to learn about probability and statistics?
Probability and statistics are fundamental tools for understanding many modern theories and techniques such as artificial intelligence, machine learning, deep learning, data mining, security, digital imagine processing and natural language processing.
What can I do after finishing learning about probability and statistics?
You will be prepared to learn modern theories and techniques to create modern security, machine learning, data mining, image processing or natural language processing software.
That sounds useful! What should I do now?
Please read this Dimitri P. Bertsekas and John N. Tsitsiklis (2008). Introduction to Probability. Athena Scientific book.
Alternatively, please watch
– this MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013 course (Lecture Notes), and
– this MIT RES.6-012 Introduction to Probability course (Lecture Notes),
and read these notes.
After finishing reading the book please click Topic 19 – Introduction to Computation and Programming Using Python to continue.
What is referencing
When writing a piece of academic work, you must acknowledge any sources you have used. You do this by including a ‘citation’ within your text (usually a number or an author’s name) next to the material you have used. This brief citation leads your reader to a full reference to the work, which you include in your list of references at the end of your text. These references should allow anyone reading your work to identify and find the material to which you have referred. You need to be consistent in the way you reference your sources by following an established referencing system and style.
Please download these 2 files for the full guide.
Please download these 2 guides for how to working with references using Microsoft Word 2007 or 2010.
If you want to use IEEE and ACM style with alphabetical (name) sequence then please download this BibWord file, unzip and copy IEEE_Alphabetical.XSL and ACMNameSeq.XSL to C:\Program Files (x86)\Microsoft Office\Office12\Bibliography\Style (The directory may be different to this in your machine).
Whenever you update your bibliography, close your document then run BibWordExtender2.exe, click “OK”, select your Word document, select Bibliography style, click “Extend”, re-open your document, re-select the style in Word.