5 Jul
2007
5 Jul
'07
7:11 a.m.
Hi,
I am the student working with Gabriela on adopting the Koschke
clustering algorithms to OO. Of the basic algorithms I have almost
implemented all of them (type-based is not really possible, and
have no idea yet if same-expression is adoptable using famix).
Most of the algorithms can be adopted quite straightforward, but
sometimes using them automatically as was the case, is out of the
question. For that reason I have build a browsable interface around
the basic steps of the clustering so the user has a bigger input.
It's main task is helping the user to wade through the mass of
information all the clustering techniques provide, as fast as possible.
Most of the algorithms aren't based on distance metrics, but
there are a few which are. I hadn't come around to implementing the
hierarchical clustering on top of the basic steps yet, so those
algorithms come quite in handy. I will check how I can incorporate
them, thanks for that Andrian.
Further questions are welcome...
Best regards,
Toon
5 Jul
5 Jul
9:17 a.m.
Sounds excellent!
Thanks for sharing that with us.
Stef
On 5 juil. 07, at 07:11, Toon Verwaest wrote:
Hi,
I am the student working with Gabriela on adopting the Koschke
clustering algorithms to OO. Of the basic algorithms I have almost
implemented all of them (type-based is not really possible, and
have no idea yet if same-expression is adoptable using famix).
Most of the algorithms can be adopted quite straightforward, but
sometimes using them automatically as was the case, is out of the
question. For that reason I have build a browsable interface around
the basic steps of the clustering so the user has a bigger input.
It's main task is helping the user to wade through the mass of
information all the clustering techniques provide, as fast as
possible.
Most of the algorithms aren't based on distance metrics, but
there are a few which are. I hadn't come around to implementing the
hierarchical clustering on top of the basic steps yet, so those
algorithms come quite in handy. I will check how I can incorporate
them, thanks for that Andrian.
Further questions are welcome...
Best regards,
Toon
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10:17 a.m.
Hi,
Indeed, it sounds very nice. Perhaps you can send us some pictures
you produced with Mondrian :).
As for types, they are in FAMIX, only the information is not
available for Smalltalk. For Java case studies, you should have the
information, but I just noticed that unfortunately iPlasma does not
export type information for methods and attributes. We should fix that.
Cheers,
Doru
On Jul 5, 2007, at 9:17 AM, Stéphane Ducasse wrote:
Sounds excellent!
Thanks for sharing that with us.
Stef
On 5 juil. 07, at 07:11, Toon Verwaest wrote:
--
www.iam.unibe.ch/~girba
www.iam.unibe.ch/~girba/blog/
"Being happy is a matter of choice."
Hi,
I am the student working with Gabriela on adopting the Koschke
clustering algorithms to OO. Of the basic algorithms I have almost
implemented all of them (type-based is not really possible, and
have no idea yet if same-expression is adoptable using famix).
Most of the algorithms can be adopted quite straightforward, but
sometimes using them automatically as was the case, is out of the
question. For that reason I have build a browsable interface around
the basic steps of the clustering so the user has a bigger input.
It's main task is helping the user to wade through the mass of
information all the clustering techniques provide, as fast as
possible.
Most of the algorithms aren't based on distance metrics, but
there are a few which are. I hadn't come around to implementing the
hierarchical clustering on top of the basic steps yet, so those
algorithms come quite in handy. I will check how I can incorporate
them, thanks for that Andrian.
Further questions are welcome...
Best regards,
Toon
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev 
3:04 p.m.
Hi guys,
Indeed, it sounds very nice. Perhaps you can send us
some pictures
you produced with Mondrian :).
Ok. We will do it!
As for types, they are in FAMIX, only the information
is not
available for Smalltalk. For Java case studies, you should have the
information, but I just noticed that unfortunately iPlasma does not
export type information for methods and attributes. We should fix that.
That can be a future work ... :) with Toon or another student :). Toon
has to finish soon (including his writing :) )
My idea was just take Koschke algorithms that were applied to procedural
programs and adapt them to object-oriented applications using purely oo
mechanisms. From my viewpoint, the use of type can bring us several
additional problems, that will give us only noise in the beginning.
That's the reason I wanted to test firstly in Smalltalk-based cases studies.
Koschke algos use simple relationships between entities in a program. We
want to extrapolate that to OO apps, and see how adaptable or not.
I have to talk to Toon, because I am not sure how far or close we are of
algos that Adrian implemented. I am sure that we are far from doing
semantic clustering, because we have read about it in the beginning of
the master thesis.
Cheers,
Gabi
10:35 p.m.
Indeed, it sounds very nice. Perhaps you can send us
some pictures
you produced with Mondrian :).
The most spectacular ones are:
http://infogroep.be/~tverwaes/visualization.gif
which shows different clusters and the parts of the hierarchies in a
program represented by those clusters. Clusters have reasons to
cluster elements together, and those boxes outside the box with the
hierarchy represent those reasons. The bigger the reason-box, the
bigger theamount of classes it represents.
The darker the classes in the hierarchies are, the more reasons support
this class in the cluster.
If a class is green it means that a superclass as well as a subclass
are present in the cluster, but not the class itself. I call these
ghostclasses since they probably should be present in the cluster as
well, but are not clustered by the algorithms.
The white boxes in the top with yellow rays are classes not available
in the cluster, but classes which are needed for the cluster to be
usable (superclasses from which some classes in the cluster need to
inherit).
The gray ray means a cluster supports a class, while a blue ray means
the whole subhierarchy is supported (a way to limit the amount of
rays..)
http://infogroep.be/~tverwaes/visualization2.gif
something I am currently working on, which places clusters in the
bigger picture. This specific picture clusters classes according to
the namespace in which they were defined.
The "best" layout available for something like this appeared the
horizontalDominance, that's the reason for the funky layout.
Greetz.
6139
days inactive
6139
days old
4 comments
4 participants
participants (4)
-
Gabriela Arevalo -
Stéphane Ducasse -
Toon Verwaest -
Tudor Girba