poster

An initial characterization of bug-injecting development sessions

Authors:

Irina Diana Coman,

Alberto Sillitti,

Giancarlo SucciAuthors Info & Claims

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

Pages 129 - 130

https://doi.org/10.1145/3183440.3195069

Published: 27 May 2018 Publication History

Get Access

Abstract

Even experienced developers rigorously testing their code and using state of the art tools and practices, inject every now and then bugs into the code. There is a huge amount of literature about the characterization of such bugs including the effectiveness of the reports and the fixes, the time required to fix them, etc. Existing works have already identified several factors considered to influence directly the bug injection. However, there is no support for the claims made so far using data coming from industrial, bug-injecting development sessions. This paper aims at filling this gap by analyzing industrial bug-injecting development sessions from several points of view. It investigates 49 bug-injecting development sessions evaluating and discussing three alleged, developers-centered main causes of bug injection: expertise, knowledge of code, and distraction. Additionally, the paper provides insights into the complete lifetime of bugs from injection to the fix and discusses implications for bug prediction.

References

[1]

A. Bykov, V. Ivanov, A. Rogers, A. Shunevich, A. Sillitti, G. Succi, A. Tormasov, J. Yi, A. Zabirov, D. Zaplatnikov. A New Architecture and Implementation Strategy for Non-Invasive Software Measurement Systems., In 33rd ACM Symposium on Applied Computing (SAC 2018), Pau, France, 9 - 13 April 2018.

Digital Library

Google Scholar

[2]

I. D. Coman, A. Sillitti. An Empirical Exploratory Study on Inferring Developers' Activities from Low-Level Data. In 19th International Conference on Software Engineering and Knowledge Engineering (SEKE 2007), Boston, MA, USA, 9 - 11 July 2007.

Google Scholar

[3]

I. D. Coman, A. Sillitti. Automated Identification of Tasks in Development Sessions. In 16th IEEE International Conference on Program Comprehension (ICPC 2008), Amsterdam, The Netherlands, 10 - 13 June 2008.

Digital Library

Google Scholar

[4]

I. D. Coman, A. Sillitti, G. Succi. A case-study on using an Automated In-process Software Engineering Measurement and Analysis system in an industrial environment. In 31st International conference on Software Engineering (ICSE 2009), Vancouver, BC, Canada, 16 - 24 May 2009

Digital Library

Google Scholar

[5]

I. D. Coman., P. N. Robillard, A. Sillitti, G. Succi. Cooperation, Collaboration and Pair-Programming: Field Studies on Back-up Behavior. In Journal of Systems and Software, Elsevier, Vol. 91, No. 5, pp. 124 -- 134, May 2014.

Digital Library

Google Scholar

[6]

E. Di Bella, A. Sillitti, G. Succi. A multivariate classification of open source developers. In Information Sciences, Elsevier, Vol. 221, pp. 72 -- 83, February 2013.

Digital Library

Google Scholar

[7]

S. Kim, T. Zimmermann, K. Pan, and E. James Jr. Whitehead. Automatic identification of bug-introducing changes. In ASE '06: Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering, pages 81--90, Washington, DC, USA, 2006.

Digital Library

Google Scholar

[8]

T. Remencius, A. Sillitti, G. Succi. Assessment of software developed by a third-party: A case study and comparison. In Information Sciences, Elsevier, Vol. 328, pp. 237 -- 249, January 2016.

Digital Library

Google Scholar

[9]

J. Sliwerski, T. Zimmermann, and A. Zeller. When do changes induce fixes? In MSR '05: Proceedings of the 2005 international workshop on Mining software repositories, pages 1--5, New York, NY, USA, 2005. ACM.

Digital Library

Google Scholar

Cited By

View all

Shah M(2019)Lib Metamorphosis: A Performance Analysis Framework for Exchanging Data Structures in Performance Sensitive Applications2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2019.00063(379-381)Online publication date: Sep-2019
https://doi.org/10.1109/ICSME.2019.00063

Index Terms

An initial characterization of bug-injecting development sessions
1. General and reference
  1. Cross-computing tools and techniques
    1. Empirical studies
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis

Recommendations

Not all bug reopens are negative: A case study on eclipse bug reports
Highlights
- A novel concept of non-negative bug reopens is proposed.
- A practical approach ...
Abstract Context
We observed a special type of bug reopen that has no direct impact on the user experience or the normal operation of the system being developed. We refer to these as non-negative bug reopens.
...
An Empirical Study on Factors Impacting Bug Fixing Time
WCRE '12: Proceedings of the 2012 19th Working Conference on Reverse Engineering

Fixing bugs is an important activity of the software development process. A typical process of bug fixing consists of the following steps: 1) a user files a bug report, 2) the bug is assigned to a developer, 3) the developer fixes the bug, 4) changed ...
Empirical Evaluation of Hunk Metrics as Bug Predictors
IWSM '09 /Mensura '09: Proceedings of the International Conferences on Software Process and Product Measurement

Reducing the number of bugs is a crucial issue during software development and maintenance. Software process and product metrics are good indicators of software complexity. These metrics have been used to build bug predictor models to help developers ...

Comments

Information & Contributors

Information

Published In

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

May 2018

231 pages

ISBN:9781450356633

DOI:10.1145/3183440

Conference Chair:
Michel Chaudron
Chalmers University of Technology, University of Gothenburg, Sweden
,
General Chair:
Ivica Crnkovic
Chalmers University of Technology, University of Gothenburg, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

Check for updates

Author Tags

Qualifiers

Poster

Conference

ICSE '18

Sponsor:

SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 27 - June 3, 2018

Gothenburg, Sweden

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

1
Total Citations
View Citations
71
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Shah M(2019)Lib Metamorphosis: A Performance Analysis Framework for Exchanging Data Structures in Performance Sensitive Applications2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2019.00063(379-381)Online publication date: Sep-2019
https://doi.org/10.1109/ICSME.2019.00063

View Options

Login options

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

Cited By

Index Terms

Recommendations

Not all bug reopens are negative: A case study on eclipse bug reports

An Empirical Study on Factors Impacting Bug Fixing Time

Empirical Evaluation of Hunk Metrics as Bug Predictors