SEKE 2012 Proceedings - Knowledge Systems Institute

trategy, the results should support fairly reliable conclusion.
4 Related Work
Current researches in CIA have varied from relying on
static information [13, 15, 6, 10] to dynamic information
[7, 1] in working out the impact set. Our technique mainly
focuses on static analysis of the program. Static CIA techniques
take all possible behaviors and inputs into account,
and they are often performed by analyzing the syntax and
semantic dependence of the program [10]. The static analysis
includes textual analysis, historical analysis and structural
static analysis. Textual analysis is an approach which
extracts some conceptual dependence (conceptual coupling)
based on the analysis of the non-source code (comments).
These coupling measures provide a new perspective to traditional
structural coupling measures. Concept coupling
is based on measuring the degree to which the identifiers
and comments from each other [13, 5]. Historical analysis
is performed by mining the information from multiple
evolutionary versions in software historical repositories
[16]. With this technique, some evolutionary dependencies
between program entities that can not be distilled by
traditional program analysis technique can be mined from
these repositories. Evolutionary dependencies show that
which entities are (historically) changed together in software
repositories, these entities may need to change when
one (some) of the entities are changed during future software
evolution. CIA is then supported based on these evolutionary
dependencies. Structural static dependencies between
program entities are very crucial to CIA, i.e., if a
program entity changes, other dependent entities might also
have to change [15, 10]. This paper is proposed based on
the structural static analysis to compute the impact set.
5 Conclusion and Future Work
This paper proposed a novel and effective CIA technique
which is based on call graph mining. Compared with traditional
call graph based CIA technique, our CIA approach
is particularly suitable for tackling multiple changes which
have interference among them, and computes a more precise
impact set with relative small-size set. Though we have
shown the effectiveness of our technique through a real empirical
study, it can not indicate its generality for other real
environment. And we will conduct experiments on other
arbitrary programs to evaluate the generality of our technique.
In addition, we would like to prioritize the entities in
the impact set to better facilitate its practical use.
References
[1] T. Apiwattanapong, A. Orso, and M. J. Harrold. Efficient
and precise dynamic impact analysis using execute after sequences.
In Proceedings of the International Conference on
Software Engineering, pages 432 – 441, 2005.
[2] R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information
Retrieval. Addison-Wesley Longman Publishing Co.,
Inc., Boston, MA, USA, 1999.
[3] S. Bohner and R. Arnold. Software Change Impact Analysis.
IEEE Computer Society Press, Los Alamitos, CA,USA,
1996.
[4] M. Gethers and D. Poshyvanyk. Using relational topic models
to capture coupling among classes in object-oriented
software systems. In Proceedings of the 2010 IEEE International
Conference on Software Maintenance, pages 1–10,
2010.
[5] H. Kagdi, M. Gethers, D. Poshyvanyk, and M.Collard.
Blending conceptual and evolutionary couplings to support
change impact analysis in source code. In Proceedings of the
IEEE Working Conference on Reverse Engineering, pages
119–128, 2010.
[6] J. Law and G. Rothernel. Whole program path-based dynamic
impact analysis. In Proceedings of the International
Conference on Software Engineering, pages 308 – 318,
2003.
[7] O. V. Lhotak. Comparing call graphs. In Workshop on Program
Analysis for Software Tools and Engineering, pages 37
– 42, 2007.
[8] B. Li, X. Sun, H. Leung, and S. Zhang. A survey
of code-based change impact analysis techniques. Journal
of Software Testing, Verification and Reliability, doi:
10.1002/stvr.1475, 2012.
[9] A. Mithun and R. Brian. Practical change impact analysis
based on static program slicing for industrial software systems.
In Proceedings of the 33rd International Conference
on Software Engineering, pages 746–755, 2011.
[10] A. Orso, T. Apiwattanapong, J. Law, G. Rothermel, and
M. J. Harrold. An empirical comparison of dynamic impact
analysis algorithms. In Proceedings of the International
Conference on Software Engineering, pages 491 – 500,
2004.
[11] A. Orso and M. J. Harrold. Leveraging field data for impact
analysis and regression testing. In Proceedings of the ACM
SIGSOFT Symposium on Foundations of Software Engineering,
pages 128–137, 2003.
[12] D. Poshyvanyk, A. Marcus, R. Ferenc, and T. Gyimothy.
Using information retrieval based coupling measures for impact
analysis. Empirical Software Engineering, 14(1):5 – 32,
2009.
[13] X. Sun, B. Li, S. Zhang, C. Tao, X. Chen, and W. Wen.
Using lattice of class and method dependence for change
impact analysis of object oriented programs. In Proceedings
of the Symposium on Applied Computing, pages 1444–1449,
2011.
[14] T. Zimmermann, A. Zeller, P. Weissgerber, and S. Diehl.
Mining version histories to guide software changes. IEEE
Transactions on Software Engineering, 31(6):429 – 445,
2005.
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