research-article

Evolution-aware monitoring-oriented programming

Authors:

Owolabi Legunsen,

Grigore RoşuAuthors Info & Claims

ICSE '15: Proceedings of the 37th International Conference on Software Engineering - Volume 2

Pages 615 - 618

Published: 16 May 2015 Publication History

Abstract

Monitoring-Oriented Programming (MOP) helps develop more reliable software by means of monitoring against formal specifications. While MOP showed promising results, all prior research has focused on checking a single version of software. We propose to extend MOP to support multiple software versions and thus be more relevant in the context of rapid software evolution. Our approach, called eMOP, is inspired by regression test selection---a well studied, evolution-centered technique. The key idea in eMOP is to monitor only the parts of code that changed between versions. We illustrate eMOP by means of a running example, and show the results of preliminary experiments. eMOP opens up a new line of research on MOP---it can significantly improve usability and performance when applied across multiple versions of software and is complementary to algorithmic MOP advances on a single version.

References

[1]

P. Avgustinov, J. Tibble, and O. de Moor. Making trace monitors feasible. In OOPSLA. 2007.

Digital Library

[2]

H. Barringer, D. Rydeheard, and K. Havelund. Rule systems for run-time monitoring: from Eagle to RuleR. In RV, 2007.

Digital Library

[3]

E. Bodden, L. Hendren, and O. Lhoták. A staged static program analysis to improve the performance of runtime monitoring. In ECOOP, 2007.

Digital Library

[4]

E. Bodden, P. Lam, and L. Hendren. Finding programming errors earlier by evaluating runtime monitors ahead-of-time. In FSE, 2008.

Digital Library

[5]

F. Chen and G. Roşu. Towards monitoring-oriented programming: A paradigm combining specification and implementation. In RV, 2003.

[6]

R. Dyer, H. Rajan, H. A. Nguyen, and T. N. Nguyen. Mining billions of AST nodes to study actual and potential usage of Java language features. In ICSE, 2014.

Digital Library

[7]

Ekstazi. http://ekstazi.org/.

[8]

M. Gabel and Z. Su. Symbolic mining of temporal specifications. In ICSE, 2008.

Digital Library

[9]

M. Gligoric, L. Eloussi, and D. Marinov. Ekstazi: Lightweight test selection. In ICSE Demo, 2015.

Digital Library

[10]

JavaMOP4. http://fsl.cs.illinois.edu/index.php/JavaMOP4.

[11]

C. Lee, D. Jin, P. O. Meredith, and G. Roşu. Towards categorizing and formalizing the JDK API. Technical Report http://hdl.handle.net/2142/30006, Computer Science Dept., UIUC, 2012.

[12]

F. Logozzo, S. K. Lahiri, M. Fähndrich, and S. Blackshear. Verification modulo versions: Towards usable verification. In PLDI, 2014.

Digital Library

[13]

Q. Luo, F. Hariri, L. Eloussi, and D. Marinov. An empirical analysis of flaky tests. In FSE, 2014.

Digital Library

[14]

Q. Luo, Y. Zhang, C. Lee, D. Jin, P. O. Meredith, T. F. Şerbănuţă, and G. Roşu. RV-Monitor: Efficient parametric runtime verification with simultaneous properties. In RV, 2014.

[15]

P. Meredith, D. Jin, F. Chen, and G. Rosu. Efficient monitoring of parametric context-free patterns. In ASE, 2008.

Digital Library

[16]

A. Orso, N. Shi, and M. J. Harrold. Scaling regression testing to large software systems. In FSE, 2004.

Digital Library

[17]

S. P. Reiss. Tracking source locations. In ICSE, 2008.

Digital Library

[18]

X. Ren, F. Shah, F. Tip, B. G. Ryder, and O. Chesley. Chianti: A tool for change impact analysis of Java programs. In OOPSLA. 2004.

Digital Library

[19]

A. Wasylkowski and A. Zeller. Mining temporal specifications from object usage. ASE, 2009.

Digital Library

[20]

W. Weimer and G. C. Necula. Mining temporal specifications for error detection. In TACAS, 2005.

Digital Library

[21]

C. C. Williams and J. K. Hollingsworth. Automatic mining of source code repositories to improve bug finding techniques. TSE, 31(6), 2005.

Digital Library

[22]

S. Yoo and M. Harman. Regression testing minimization, selection and prioritization: A survey. STVR, 22(2), 2012.

Digital Library

[23]

L. Zhang, D. Marinov, L. Zhang, and S. Khurshid. Regression mutation testing. In ISSTA, 2012.

Digital Library

Cited By

Legunsen OHassan WXu XRoşu GMarinov DLo DApel SKhurshid S(2016)How good are the specs? a study of the bug-finding effectiveness of existing Java API specificationsProceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering10.1145/2970276.2970356(602-613)Online publication date: 25-Aug-2016
https://dl.acm.org/doi/10.1145/2970276.2970356
Legunsen OHariri FShi ALu YZhang LMarinov DZimmermann TCleland-Huang JSu Z(2016)An extensive study of static regression test selection in modern software evolutionProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2950290.2950361(583-594)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1145/2950290.2950361

Index Terms

Evolution-aware monitoring-oriented programming

Recommendations

Multiobjective differential evolution algorithm based on decomposition for a type of multiobjective bilevel programming problems

This paper considers the multiobjective bilevel programming problem (MOBLPP) with multiple objective functions at the upper level and a single objective function at the lower level. By adopting the Karush-Kuhn-Tucker (KKT) optimality conditions to the ...
Middleware specialization using aspect oriented programming
ACMSE '06: Proceedings of the 44th annual ACM Southeast Conference

Standardized middleware is used to build large distributed real-time and enterprise (DRE) systems. These middleware are highly flexible and support a large number of features since they have to be applicable to a wide range of domains and applications. ...
Bi-goal evolution for many-objective optimization problems

This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed that 1) the conflict between ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '15: Proceedings of the 37th International Conference on Software Engineering - Volume 2

May 2015

1058 pages

General Chair:
Antonia Bertolino
ISTI-CNR, Italy
,
Program Chairs:
Gerardo Canfora
University of Sannio, Italy
,
Sebastian Elbaum
University of Nebraska-Lincoln

Sponsors

ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\DATC: IEEE Computer Society
TCSE: IEEE Computer Society's Tech. Council on Software Engin.

Publisher

IEEE Press

Publication History

Published: 16 May 2015

Check for updates

Qualifiers

Research-article

Conference

ICSE '15

Sponsor:

ACM
SIGSOFT
IEEE-CS\DATC
TCSE

ICSE '15: 37th International Conference on Software Engineering

May 16 - 24, 2015

Florence, Italy

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
85
Total Downloads

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

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Legunsen OHassan WXu XRoşu GMarinov DLo DApel SKhurshid S(2016)How good are the specs? a study of the bug-finding effectiveness of existing Java API specificationsProceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering10.1145/2970276.2970356(602-613)Online publication date: 25-Aug-2016
https://dl.acm.org/doi/10.1145/2970276.2970356
Legunsen OHariri FShi ALu YZhang LMarinov DZimmermann TCleland-Huang JSu Z(2016)An extensive study of static regression test selection in modern software evolutionProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering10.1145/2950290.2950361(583-594)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1145/2950290.2950361

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents