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I'm looking for research papers or studies made on Unit Testing and TDD effectiveness.
Points of interest:
Does TDD reduce Development time?
Does overall development cost reduced as well?
Is the result product more stable?
StudiesOfTestDrivenDevelopment has a summary and more links.
Microsoft Research: Realizing quality improvement through test driven
development: results and experiences of four industrial
teams
An article on infoQ: Empirical Studies Show Test Driven Development Improves Quality.
And a link to the Realizing quality improvement through TDD study.
"TDD seems to be applicable in various domains and can significantly reduce the defect density of developed software without significant productivity reduction of the development team." The study compared 4 projects, at Microsoft and IBM that used TDD with similar projects that did not use TDD.
http://www.eclipsecon.org/summiteurope2007/presentations/ESE2007_TDD-case_study.pdf
http://www.thycotic.com/casestudy4.html
Take a look at papers by Nancy Van Schooenderwoert (et al) at www.agilerules.com. In particular "Embedded Agile Project by the Numbers With Newbies"
http://www.slideshare.net/ZendCon/test-driven-development-presentation has a nice presentation explaining TDD though it is geared to Zend and PHP.
http://biblio.gdinwiddie.com/biblio/StudiesOfTestDrivenDevelopment has a number of relevant links
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I am trying to learn how to employ Test Driven Development into my coding practices. Are there any blogs out there that anyone can recommend that regularly posts information about using TDD?
Bob Martin blogs here now - well worth following: http://blog.8thlight.com/uncle-bob/archive.html
Also Jason Gorman: http://codemanship.co.uk/parlezuml/blog/
and the Growing Object Oriented Software group: https://groups.google.com/forum/#!forum/growing-object-oriented-software
If you are prepared to buy a book Roy Osherove's book 'The Art of Unit Testing' is a great read when starting out with TDD.
Roy also has a blog here.
Uncle Bob has some great blog posts here.
A colleague of mine has some very clear concise blog articles on TDD here.
If you don't know what a code kata is have a look at at the kata catalogue at http://codingdojo.org/. Code kata's are a great way to practice TDD.
I have written a blog post on solving the FizzBuzz kata, it's a very simple kata, but in my post my aim is to demonstrate how to go about doing TDD test first. You can see the blog post here.
There are also some great TDD videos up on PluralSight although you would need to pay for these.
Learning to do TDD is a journey and can be pretty frustrating to get right to start with, but if you do get it right the rewards are massive so I would suggest giving it a real go.
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I saw a talk by Keith Adams of Facebook comparing machine learning techniques to tuning code for improved performance in the real world. Are there examples of such automation techniques being applied in real projects? I
I know of profile guided optimizations in certain compilers and also some techniques JIT compilers use to improve performance, but I am thinking of more fundamental ways to improve code performance that could require changing the code itself and not code generation. Things like:
Choosing the optimal buffer size in a particular network application or choosing the right stack size for particular application.
Selecting the struct layout in a multi-threaded application that improves local cache performance while reducing false sharing.
Selecting different data structures all together for a certain algorithm.
I read a paper on Halide, an image processing framework that uses genetic algorithms to auto-tune image processing pipelines to improve performance. Examples like this one or any pointers to research would be useful.
Have a look at Remy http://web.mit.edu/remy/
It uses kind of genetic optimization approach to generate algorithm for congestion control in networks, significantly increasing network's performance. One specifies assumptions about network being used, and Remy generates control algorithm to be run on data nodes of this network. The results are amazing, Remy outperforms all human-developed so far optimization techniques.
FFTW is a widely used software package which uses OCaml to generate optimized C code. This paper has more details on the process: http://vuduc.org/pubs/vuduc2000-fftw-dct.pdf
You might also look into Acovea, a genetic algorithm to optimize compiler flags: http://stderr.org/doc/acovea/html/index.html
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I hoping to create an application that would listen to ambient sound and detect if music is being played. It is not important to identify the music being played; just detecting that some music is being played is enough.
I looked around for existing solutions but couldn't find any. Does anyone know algorithms that I can use to solve this problem? If source code is available, all the better.
I found are a couple of academic papers and implemented solutions suggested in them. But the results I obtained were not satisfactory.
PS:
i) It would be a bonus if the algorithm is not computationally intensive; if algorithm is completely in time-domain that would be wonderful. ii) It is okay if the solution is not very accurate; occasional false-positives are okay.
Under the assumption that music is made of a bunch of chords instead of single pitch (like monophonic MIDI), multiple pitches at the same time (aka, the chords) may be a good candidate to be detected and differentiated from pure noise. Actually there is a very good Harmony Progression Analyser software package in which chords are detected based on a chromagram. Hope it helps.
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I was wondering if anyone had knew of a website that provides a great review of data structures and algorithms. I would like it to specifically geared towards interview questions with regards to data structures and algorithms. Would implementation of all of these data structures be something good to review?
Thanks!
This page is a good starting point:
This webpage covers the space and time Big-O complexities of common algorithms used in Computer Science. When preparing for technical interviews in the past, I found myself spending hours crawling the internet putting together the best, average, and worst case complexities for search and sorting algorithms so that I wouldn't be stumped when asked about them. Over the last few years, I've interviewed at several Silicon Valley startups, and also some bigger companies, like Yahoo, eBay, LinkedIn, and Google, and each time that I prepared for an interview, I thought to msyelf "Why oh why hasn't someone created a nice Big-O cheat sheet?". So, to save all of you fine folks a ton of time, I went ahead and created one.
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I'm new to web programming. Where is the best place online can I find examples of algorithms created to solve specific problems ie. reputation system for a user controlled forum.
On my opinion a good introduction to this issue you can fing in this book:
Paperback: 336 pages
Publisher: Yahoo Press; 1 edition (March 16, 2010)
Language: English
ISBN-10: 059615979X
ISBN-13: 978-0596159795
Table of Contents:
Part I. Reputation Defined and Illustrated
Reputation Systems Are Everywhere
A (Graphical) Grammar for Reputation
Part II. Extended Elements and Applied Examples
Building Blocks and Reputation Tips
Common Reputation Models
Planning Your System’s Design
Objects, Inputs, Scope, and Mechanism
Displaying Reputation
Using Reputation: The Good, The Bad, and the Ugly
Application Integration, Testing, and Tuning
Case Study: Yahoo! Answers Community Content Moderation
Here you can buy it: http://www.amazon.com/Building-Reputation-Systems-Randy-Farmer/dp/059615979X