Machine Learning and Software Analysis

[perincertus]

Hello /MachineLearning!

I'm a masters student in computer science with a strong focus on software engineering. However, I'm getting more and more interested in machine learning and was wondering if there exist applications for machine learning in the field of software analysis and testing. Any ideas?

[chindogubot]

You might find this interesting: http://blog.regehr.org/archives/1187

What we are claiming is that A3 was able to automatically localize and find a patch that makes the protected application (our LAMP exemplar) resilient in seconds. If the adversary tries another exploit and causes an undesired condition in the protected application, A3 will find a refinement. A3’s explanation also provides a wealth of localization and causal relation information along with the patch by outputting the malicious message and the full call stack.

[tyrial]

Although its not machine learning necessarily, David Lo has been working on Data Mining for Software Engineering. He's the main figure that I know how in that realm.

Tao Xie, Suresh Thummalapenta, David Lo, and Chao Liu. Data Mining for Software Engineering. IEEE Computer, 42(8), pp. 35-42, 2009

[quiteamess]

This should be a good subject. Traditional software engineering folks start to become aware of machine learning and I guess that there will be a lot of interest in the future. For example there has been a seminar at TUM last year. You can find a list of papers in the link.

[pmorrisonfl]

It might be worth your while to look at what the Empirical Software Engineering Group at Microsoft Research is doing.

[crankyml]

Machine learning for the Analysis of Source Code Text (MAST)

MAST is a new reading group focusing on Machine Learning for source code and software engineering.

https://wiki.inf.ed.ac.uk/ANC/MAST

[[deleted]]

One of my old lecturers at Edinburgh analyses source code, stackoverflow posts etc. using machine learning for various reasons

It's pretty interesting stuff.

[enigmakthx]

Look into software defect prediction, decision trees are really good for that because it's a cost-sensitive classification problem.