SEKE 2012 Proceedings - Knowledge Systems Institute

function until he could find the method, this would be a ver y
hard task that could take several days or even weeks of work.
In [18], another approach to summ arize traces was
designed using fan-in and fan-out metrics, which is completely
different from the specialized filtering approach used in this
work. In their approach, the functions are ranked considering
their relative importance based on graph metrics which do not
consider any semantic information of the respective function.
The approach of program comprehension presented in this
work guides the developer in a much more precise manner,
because it is the developer itself that have the tacit knowledge
of what he really needs and thus, he writes the proper filters
considering this kn owledge. Consequently, this strategy
improved dramatically the quality of the information available
in the trace. However, it is important to note that our approach
requires a more specialized preparation of the trace filtering.
Although, the developer can reuse th e generic parts of our
filtering framework, he stills needs to grasp a filtering solution
that will provide a precise “fishing” of the desired functions.
Some authors [19, 20] suggest the integrated use of static
and dynamic views of the software system in program
comprehension activities. The dynamic views are obtained by
profiling the most used features in the system. Nonetheless,
the primary goal is to obtain the system architecture to reduce
the effort of comprehension of the system. In our approach, we
have not focused on the most used features for comprehending
the system in an overall manner. We have focused on a well
chosen feature in order to prov ide direct information
customized for the process o f program comprehension. This
requires less effort to m ake further needed changes in the
source code. Nonetheless, we could still improve our process
of writing adequate filters and even o f browsing the resulted
traces with static information. For in stance, the use of
information retrieval techniques could also be in corporated
[21, 22, 23], because we could see th at the function names
used in kernel are very representative and similar to the
developer terms in high-level communication.
VI. FINAL REMARKS
This paper has presented an innovative study in the program
comprehension area because it u sed execution trace
information to isolate desired functions in the Linux operating
system kernel. Previous studies in this area did not handled OS
kernels.
Our findings have shown that despite the huge a mount of
events that an instrumentation tool can generate, especially in
the case of OS kernel which has to cope with much more kinds
of different low-level events than a high-level application, the
use of proper filtering mechanisms in the instrumentation tool
can reduce dramatically the complexity of the execution trace
information. This scenario reduces the developer effort during
maintenance tasks when he needs to find specific functions in
the source code. Future work includes reproducing the
subjacent methodology designed in this work in a large scale
experiment to produ ce stronger evidences on our findings.
Also, introducing hybrid static techniques in a co hesive
methodology is an important step.
REFERENCES
[1] M. Florian. Linux Kernel Statistics. [Online]. Available at:
http://www.schoenitzer.de/lks/lks_en.html. A ccessed in 2011,
December.
[2] Kernel development statistics for 2.6.35. [Online]. Available at:
http://lwn.net/Articles/395961/. Accessed in 2011, December.
[3] B. Dit, M. Revelle, M. Gethers, D. Poshyvanyk. “Feature location in
source code: a taxonomy and survey”. Journal of Software Maintenance
and Evolution: Research and Practice. In press. Published online. 2011.
[4] B. Cornelissen, A. Zaidman, A. van Deursen, L. Moonen, R. Koschke,
“A Systematic Survey of Program Comprehension Through Dynamic
Analysis,” in IEEE Transactions on Software Engineering,Vol.35, 2009,
pp. 684–702.
[5] N. Wilde, M. Scully “Software Reconnaissance: mapping program
features to code”. Software Maintenance: Research and Practice, vol. 7,
n. 1, pp. 49-62, 1995.
[6] The Linux Documentation Project. [Online]. Disponível: http://tldp.org/.
Accessed in 2011, December.
[7] I. T. Bowman, R. Holt, N. Brewster: Linux as a Case Study: Its
Extracted Software Architecture. In Proc. of ICSE, pp. 555-563, 1999.
[8] D. P. Bovet and M. Cesati, “Understanding The Linux Kernel,” 3 th ed.
O’Reilly, 2005.
[9] R. Love, “Linux Kernel Development Second Edition”, Pearson
Education, 2th ed. 2005.
[10] SystemTap. [Online]. Available at: http://sourceware.org/systemtap/.
Accessed in 2011, December.
[11] R. Matias, I. Becker, B. Leitão, P.R. Maciel “Measuring Software Aging
Effects Through OS Kernel Instrumentation,” in IEEE Second
International Workshop on Software Aging and Rejuvenation, San Jose,
CA, 2010, pp. 1-6.
[12] S. Rostedt, “Finding Origins of Latencies Using Ftrace,” in Proc. of
the11th Real Time Linux Workshop, 2009.
[13] Documentation Ftrace. [Online]. Available at: http://www.mjmwired.
net/kernel/Documentation/trace/ftrace.txt. Accessado in 2011,
November.
[14] Operating System Family Share. November, 2011. [Online]. Available
at: http://www.top500.org/charts/list/38/osfam
[15] Intel® 64 and IA-32 Architectures Software Developer’s Manual,
Volume 3A: System Programming Guide, March 2010.
[16] H. Yan, D. Garlan, B. Schmerl, J. Aldrich, and R. Kazman. DiscoTect:
A System for Discovering Architectures from Running Systems. In Proc.
of ICSE, pp. 470-479, 2004.
[17] L. Silva and K. Paixão and S. Amo and M. Maia, "On the Use
of Execution Trace Alignment for Driving Perfective Changes”, In the
15 th European Conference on Software Maintenance and Reengineering,
pp. 221-230, 2011.
[18] A. Hamou-Lhadj and T. Lethbridge, "Summarizing the Content of Large
Traces to F acilitate the U nderstanding o f the Be haviour o f a
Software System”, In Proceedings of the 14th IEEE International
Conference on Program Comprehension, IEEE Computer Society, pp.
181-190, 2006.
[19] M. Mit and M. Ernst. Static and Dynamic Analysis: Synergy and
Duality. In Proc. of ICSE, pp 24-27, 2003.
[20] K. Sartipi, and N. Dezhkam. An Almalgamated Dynamic and Static
Architecture Reconstruction Framework to Control Component
Interactions. In Proc. of WCRE, pp 259-268, 2007.
[21] S. K. Lukins, N. A. Kraft, and L. H. Etzkorn, “Bug localization using
latent Dirichlet allocation,” Information and Software Technology,
vol. 52, no. 9, pp. 972 – 990, 2010.
[22] J. Maletic and A. Marcus, “Support for software m aintenance
using latent semantic analysis,” in Proc. 4th Annual IASTED
International C onference on Software Engineering and
Applications (SEA’00), Las Vegas, 2000, pp. 250–255.
[23] A. Marcus and J. Maletic, “Recovering documentation-to-sourcecode
traceability links using latent semantic indexing,” in Proc. of
the 25 th Intl. Conf. on Software Engineering, 2003, pp. 125 – 135.
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