article

Dynamic structure measurement for distributed software

Authors:

Jianlei ChiAuthors Info & Claims

Software Quality Journal, Volume 26, Issue 3

Pages 1119 - 1145

https://doi.org/10.1007/s11219-017-9369-3

Published: 01 September 2018 Publication History

Abstract

With the advent of network technologies and the ultra-fast increasing of computing ability, the distributed architecture has become a necessity for the majority of software systems. However, it is difficult for current architecture measurements to evaluate distributed systems, such as cohesion and coupling. Most current methods focus on the relations among various classes or packages but barely consider the structure at component level, which has a serious impact on change impact analysis, fault diagnosis, or other maintenance activities. In this paper, we propose a dynamic structure measurement for distributed software. The intra-component and inter-component dependencies are introduced into a Calling Network model to further represent distributed software. More importantly, based on the Kieker monitoring framework, the measurement methods are proposed and implemented for distributed software. Two structural quality attributes cohesion factor of component (CHC) and coupling factor of component (CPC) are measured. Finally, case studies are conducted on two open-source distributed systems: RSS Reader Recipes and the distributed version of iBATIS JPetStore. By applying the proposed methods and comparing with the existing ones, the features of CHC and CPC can be assessed and observed for distributed software.

References

[1]

Al Dallal, J. (2010). Mathematical validation of object-oriented class cohesion metrics. International Journal of Computers, 4(2), 45-52.

[2]

Allen, E.B., Khoshgoftaar, T.M., & Chen, Y. (2001). Measuring coupling and cohesion of software modules: an information-theory approach. In International symposium on software metrics (p. 124).

[3]

Arisholm, E., Briand, L.C., & Foyen, A. (2002). Dynamic coupling measurement for object-oriented software. IEEE Transactions on Software Engineering, 30(8), 33-42.

[4]

Athanasopoulos, D., Zarras, A., Miskos, G., Issarny, V., & Vassiliadis, P. (2014). Cohesion-driven decomposition of service interfaces without access to source code. IEEE Transactions on Services Computing, 8(4), 1-1.

[5]

Bavota, G., Dit, B., Oliveto, R., Penta, M.D., Poshyvanyk, D., & Lucia, A.D. (2013). An empirical study on the developers' perception of software coupling. In International conference on software engineering (pp. 692-701).

[6]

Bieman, J.M., & Ott, L.M. (1994). Measuring functional cohesion. IEEE Transactions on Software Engineering, 20(8), 644-657.

Digital Library

[7]

Briand, L.C., Daly, J.W., & Wüst, J. (1998). A unified framework for cohesion measurement in object-oriented systems. Empirical Software Engineering, 3(1), 43-53.

[8]

Briand, L.C., Morasca, S., & Basili, V.R. (1999). Defining and validating measures for object-based high-level design. IEEE Transactions on Software Engineering, 25(5), 722-743.

Digital Library

[9]

Cai, H., & Thain, D. (2016). Distia: a cost-effective dynamic impact analysis for distributed programs. In IEEE/ACM international conference on automated software engineering (pp. 344-355).

[10]

Chidamber, S.R. (1991). Towards a metrics suite for object oriented design. In Proceedings of the conference on OOPSLA'91, Sigplan Notices.

[11]

Chidamber, S.R., & Kemerer, C.F. (1994). A metrics suite for object oriented design. IEEE Transactions on Software Engineering, 20(6), 476-493.

Digital Library

[12]

Coulouris, G., Dollimore, G., Kindberg, J., & Blair, T. (2012). Distributed systems: concepts and design (5th edition).

[13]

Counsell, S., Swift, S., & Crampton, J. (2006). The interpretation and utility of three cohesion metrics for object-oriented design. ACM Transactions on Software Engineering and Methodology, 15(2), 123-149.

Digital Library

[14]

Dallal, J.A., & Briand, L.C. (2012). A precise method-method interaction-based cohesion metric for object-oriented classes. ACM Transactions on Software Engineering and Methodology, 21(2), 1-34.

[15]

Desouky, A.F., & Etzkorn, L.H. (2014). Object oriented cohesion metrics: a qualitative empirical analysis of runtime behavior. In ACM southeast regional conference (pp. 1-6).

[16]

Elish, M.O. (2010). Exploring the relationships between design metrics and package understandability: a case study. In The 18th IEEE international conference on program comprehension, ICPC 2010, Braga, Minho, Portugal, June 30-July 2, 2010 (pp. 144-147).

Digital Library

[17]

Geetika, R., & Singh, P. (2014). Empirical investigation into static and dynamic coupling metrics. ACM SIGSOFT Software Engineering Notes, 39(1), 1-8.

Digital Library

[18]

Gethers, M., & Poshyvanyk, D. (2010). Using relational topic models to capture coupling among classes in object-oriented software systems. In 26th IEEE international conference on software maintenance (ICSM2010) (pp. 1-10).

[19]

Gupta, V., & Chhabra, J.K. (2011). Dynamic cohesion measures for object-oriented software. Journal of Systems Architecture - Embedded Systems Design, 57(4), 452-462.

Digital Library

[20]

Hoorn, A.V., Waller, J., & Hasselbring, W. (2012). Kieker: a framework for application performance monitoring and dynamic software analysis. In Proceedings of the 3rd ACM/SPEC international conference on performance engineering (pp. 247-248).

[21]

Indrajit Wijegunaratnec, M., & Fernandez, G. (1998). Distributed applications engineering. London: Springer.

[22]

Jin, W., Liu, T., Qu, Y., Chi, J., Cui, D., & Zheng, Q. (2016). Dynamic cohesion measurement for distributed system. In The international workshop on specification, comprehension, testing, and debugging of concurrent programs (pp. 20-26).

[23]

Lee, Y.S., & Liang, B.S. (1995). Measuring the coupling and cohesion of an object-oriented program based on information flow. In Proceedings of international conference on software quality.

[24]

Lin, Y., Peng, X., Cai, Y., Dig, D., Zheng, D., & Zhao, W. (2016). Interactive and guided architectural refactoring with search-based recommendation. In Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering (FSE).

[25]

Marwede, N., Rohr, M., Hoorn, A., & Hasselbring, W. (2009). Automatic failure diagnosis support in distributed large-scale software systems based on timing behavior anomaly correlation. In European conference on software maintenance and reengineering (pp. 47-58).

[26]

Mathur, R., Keen, K.J., & Etzkorn, L.H. (2011). Towards a measure of object oriented runtime cohesion based on number of instance variable accesses. In Southeast regional conference 2011, Kennesaw, GA, USA (pp. 255-257).

[27]

Mitchell, Á., & Power, J.F. (2004). An empirical investigation into the dimensions of run-time coupling in Java programs. In Proceedings of the 3rd international symposium on principles and practice of programming in Java (pp. 9-14). Trinity College Dublin.

[28]

Nayrolles, M., Moha, N., & Valtchev, P. (2013). Improving soa antipatterns detection in service based systems by mining execution traces. In Working conference on reverse engineering (WCRE) (pp. 321-330).

[29]

Nguyen, H., Shen, Z., Tan, Y., & Gu, X. (2013). Fchain: toward black-box online fault localization for cloud systems. In 2013 IEEE 33rd international conference on distributed computing systems (ICDCS) (Vol. 7973, pp. 21-30).

[30]

Perepletchikov, M., & Ryan, C. (2011). A controlled experiment for evaluating the impact of coupling on the maintainability of service-oriented software. IEEE Transactions on Software Engineering, 37(4), 449-465.

Digital Library

[31]

Perepletchikov, M., Ryan, C., Frampton, K., & Tari, Z. (2007). Coupling metrics for predicting maintainability in service-oriented designs. In Australian software engineering conference (pp. 329-340).

[32]

Poshyvanyk, D., & Marcus, A. (2006). The conceptual coupling metrics for object-oriented systems. In IEEE international conference on software maintenance (pp. 469-478).

[33]

Poshyvanyk, D., Marcus, A., Ferenc, R., & Gyimóthy, T. (2009). Using information retrieval based coupling measures for impact analysis. Empirical Software Engineering, 14(1), 5-32.

Digital Library

[34]

Qu, Y., Guan, X., Zheng, Q., Liu, T., Wang, L., Hou, Y., & Yang, Z. (2015a). Exploring community structure of software call graph and its applications in class cohesion measurement. Journal of Systems and Software, 108, 193-210.

[35]

Qu, Y., Guan, X., Zheng, Q., Liu, T., Zhou, J., & Li, J. (2015b). Calling network: a new method for modeling software runtime behaviors. ACM SIGSOFT Software Engineering Notes, 40(1), 1-8.

Digital Library

[36]

Sellers, B.H. (1995). Object-oriented metrics measures of complexity. Prentice-Hall Inc.

[37]

Stevens, W.P., Myers, G.J., & Constantive, L.L. (1974). Structured design. Ibm Systems Journal, 13(2), 115-139.

Digital Library

[38]

Tahir, A., & Macdonell, S.G. (2012). A systematic mapping study on dynamic metrics and software quality. In 28th IEEE international conference on software maintenance, 2012 Vol. 9, no. 3, pp. 326-335.

[39]

Tanenbaum, A.S., & Steen, M.V. (2002). Distributed systems: principles and paradigms (pp. 279-283). Tsinghua University Press.

[40]

Thones, J. (2015). Microservices. IEEE Software, 1, 116-116.

[41]

Tian, Z., Liu, T., Zheng, Q., Zhuang, E., Fan, M., & Yang, Z. (2017). Reviving sequential program birthmarking for multithreaded software plagiarism detection. IEEE Transactions on Software Engineering. http://ieeexplore.ieee.org/abstract/document/7888597/.

[42]

Wang, H., Liu, T., Guan, X., Shen, C., Zheng, Q., & Yang, Z. (2017). Dependence guided symbolic execution. IEEE Transactions on Software Engineering, 43(3), 252-271.

Digital Library

[43]

Yacoub, S.M., Ammar, H.H., & Robinson, T. (1999). Dynamic metrics for object oriented designs. In Proceedings of the 6th international software metrics symposium, 1999 (pp. 50-50).

[44]

Ying, A.T.T., Murphy, G.C., Ng, R., & Chu-Carroll, M.C. (2004). Predicting source code changes by mining change history. IEEE Transactions on Software Engineering, 30(9), 574-586.

Digital Library

[45]

Zhou, Y., Lu, J., & Xu, H.L.B. (2004). A comparative study of graph theory-based class cohesion measures. ACM SIGSOFT Software Engineering Notes, 29(2), 13-13.

Digital Library

Cited By

Jin WZhong DCai YKazman RLiu T(2023)Evaluating the Impact of Possible Dependencies on Architecture-Level MaintainabilityIEEE Transactions on Software Engineering10.1109/TSE.2022.317128849:3(1064-1085)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1109/TSE.2022.3171288
Jin WCai YKazman RZhang GZheng QLiu TGrundy JLe Goues CLo D(2020)Exploring the architectural impact of possible dependencies in Python softwareProceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering10.1145/3324884.3416619(758-770)Online publication date: 21-Dec-2020
https://dl.acm.org/doi/10.1145/3324884.3416619
Jin WCai YKazman RZheng QCui DLiu TMussbacher GAtlee JBultan T(2019)ENREProceedings of the 41st International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion.2019.00040(67-70)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICSE-Companion.2019.00040
Show More Cited By

Recommendations

Dynamic cohesion measurement for distributed system
SCTDCP 2016: Proceedings of the 1st International Workshop on Specification, Comprehension, Testing, and Debugging of Concurrent Programs

Instead of developing single-server software system for the powerful computers, the software is turning from large single-server to multi-server system such as distributed system. This change introduces a new challenge for the software quality ...
Verification of Concurrent Control Flow in Distributed Computer Systems

An approach to verifying control flow in distributed computer systems (DCS) is presented.The approach is based on control flow checking among software components distributed over processors and cooperating among them.In this approach, control flow ...
Managing change and reliability of distributed software system

Software reliability is the end-user view of software quality. It cannot be directly measured, so other related factors such as development process, faults and failures are measured to estimate the software reliability. In this paper, an architecture-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Software Quality Journal

Software Quality Journal Volume 26, Issue 3

September 2018

326 pages

ISSN:0963-9314

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2018

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jin WZhong DCai YKazman RLiu T(2023)Evaluating the Impact of Possible Dependencies on Architecture-Level MaintainabilityIEEE Transactions on Software Engineering10.1109/TSE.2022.317128849:3(1064-1085)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1109/TSE.2022.3171288
Jin WCai YKazman RZhang GZheng QLiu TGrundy JLe Goues CLo D(2020)Exploring the architectural impact of possible dependencies in Python softwareProceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering10.1145/3324884.3416619(758-770)Online publication date: 21-Dec-2020
https://dl.acm.org/doi/10.1145/3324884.3416619
Jin WCai YKazman RZheng QCui DLiu TMussbacher GAtlee JBultan T(2019)ENREProceedings of the 41st International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion.2019.00040(67-70)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICSE-Companion.2019.00040
Fu XCai HGuéhéneuc YKhomh FSarro F(2019)Measuring interprocess communications in distributed systemsProceedings of the 27th International Conference on Program Comprehension10.1109/ICPC.2019.00051(323-334)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICPC.2019.00051

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents