Bad smells in code ?? :O

I think it is really important that you identify the "bad smells" in a code that you have written, simply since they are not good to have.

There are number of such "bad smells" which have been recognized and expected, programmers to be aware of. You can check most of them from here. 

I had a research on the internet for a Eclipse plug-in in order to identify so called "bad smells". Luckily I found this wonderful plug-in called "JDeodorant". 

JDeodorant is capable of recognizing four main types of "bad smells" that can be found in your code namely God Class, Long Method, Type Checking and Feature Envy. I have created a screen cast on "how to use JDeodorant?" and here it is.

Hope this post made your attention towards possible "bad smells" in your code, and hopefully correcting them (Oh I forgot to tell, using JDeodorant you can correct most of these bad smells as well ).

Happy Coding!! :)

Work done in the period of November 20th – December 3rd

Within this period I managed to serialize the whole drools rules file as a Knowledge Base Object, and observed a significant improvement (approximately 10s) but still far less than the execution time of the current RelEx2Frame. Also that serialization needed JVM stack size to be increased to 2MB. Still we felt that the performance is not up to the requirement so we decided to split the drools rules file according to 100 rules per file basis and Nisansa did that task. Danaja came up with a design which is focused on applying concurrency and parallelism for the RelEx2Frame system, and was accepted by all the members of the team as the basic design which will be altered and improved after further analysis.

In the current RelEx2Frame there is a significant limitation of the concepts or the words that are detected. Statistical learning methods can be used to reduce this limitation. One approach is to use an existing application and the other would be to implement statistical learner. Google Sets [1] is one of the existing applications that we are considering. During this period I have implemented an application which accesses Google Sets and generate new set of words for a given combination of words (<4). I have used an existing library called ‘XGoogle’ [2] written in Python programming language which provides an interface to access Google Sets. Since I was not familiar with Python, I had to learn Python and which I successfully managed to do. We will keep the results came out from this application and will compare with the results from our statistical learner to choose the most appropriate set of words.

Preparing the design document was the major work that we had done during this period, since it was due on 3^rd December. All of us contributed to the design document in several ways and I contributed by writing design constraints, design decisions and designing rule learning component. Design constraints part involved basically three sub sections, namely Operating Environment, End-user Environment and Performance Requirements. Design decisions consisted with major decisions, some of which were already taken and others yet to be taken. Programming language selection, rule engine selection, caching knowledge bases, statistical learning of concepts and selecting the best suited data mining algorithm were the main design considerations discussed there.

Designing the rule learner was the most challenging task to me. I read many documents [3-5] on existing rule learners, existing rule induction algorithms, data mining techniques etc.  After considerable amount of literature survey I came up with the architecture for the statistical rule learner using data mining techniques, which will be altered and improved as it requires. Chamilka reviewed it and made few suggestions.

We were successfully managed to submit the design document on 3^rd of December.

  

[1] “Google sets labs,” [Online]. Available: http://labs.google.com/sets

[2] “XGoogle,” [Online]. Available: http://www.catonmat.net/blog/python-library-for-google-sets

[3] K. Mhashilkar. “Data Mining Technology,” [Online]. Available: http://www.executionmih.com/data-mining/technology-architecture-application-frontend.php

[4] J. Grzymala-Busse, “Three strategies to rule induction from data with numerical attributes,” presented at the International Workshop on Rough Sets in Knowledge Discovery (RSKD 2003), associated with the European Joint Conferences on Theory and Practice of Software 2003, Warsaw, Poland, April 5–13, 2003.

[5] “Rule Learner,” [Online]. Available: http://openrules.com/RuleLearner.htm

Work done in the period of 6th to 19th of November

During this period my main task was to integrate the converted Drools Rules to the RelEx source code. For that first I just created a Drools State-full Knowledge session which creates the Knowledge Base based on the converted Drools Rules file. At the first debugging time, I got hell of errors, which is some what expected since we had not done any debugging of created Drools rules before that moment. So I started to look into each and every error thrown. Few errors among hundreds of errors are noted below.

Import statement was missing from the drools rules file: I altered the RuleConverter code such that it adds that.

There were lot of typos found in the RelEx2Frame hand written rule file, which mislead our RuleConverter to generate malformed rules: I debugged and edited the hand written rule file as needed.

Once I made the Drools to create the KnowledgeBase successfully, I started to debug the methods used inside the Drools rules which were implemented by Danaja and Nisansa. They didn't have a way to debug there method earlier, so we expected few bugs from those methods. I found few bugs in one of the methods (which is the tricky method), and successfully fixed those bugs, and got the rules to work.

Then I found out that currently we were not outputting the given sentence related output but only the rule. So I went ahead and implemented that functionality. It needed few changes in the Drools rules file (i.e. RuleConverter) and also in few methods in the RelEx2Frame. While doing this I observed that the 'then' part of the rule is not get executed suddenly after the activation of a rule. I had a discussion about this behaviour in Rules Users List mailing list and got to know the following:

“ In the rete algorithm the agenda is a list of activated rules who's actions are eligible to fire. The "first" one on the list is selected, it's action is fired, and the agenda might change as a result. "First" is in quotes because the agenda list is sorted by conflict resolution rules.”

So I altered the code a bit such that it solves this issue as well.

I did few tests with the new Rule Engine, and found out it takes ages (10-15mins) to display the resulted frame outputs. I raised this issue in front of my group mates and made the point that a possible approach of caching which I have seen in few mailing list discussions.

Further I edited the existing RelEx build.xml file such that it adds necessary Drools related executables to the classpath before compiling the source code.

We had few discussions on preparing the design document as well, again Chamilka took the lead and cooperated with others and divided separate parts among four of us.