An Empirical Study on Factors Impacting Bug Fixing Time Feng Zhang, Foutse Khomh, Ying Zou and Ahmed E. Hassan
An Empirical Study on Factors Impacting Bug Fixing Time
Jul 05, 2015
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 code is reviewed and verified; and 5) the bug is resolved. Many studies have investigated the process of bug fixing. However, to the best of our knowledge, none has explicitly analyzed the interval between bug assignment and the time when bug fixing starts. After a bug assignment, some developers will immediately start fixing the bug while others will start bug fixing after a long period. We are blind on developer’s delays when fixing bugs. This paper explores such delays of developers through an empirical study on three open source software systems. We examine factors affecting bug fixing time along three dimensions: bug reports, source code involved in the fix, and code changes that are required to fix the bug. We further compare different factors by descriptive logistic regression models. Our results can help development teams better understand factors behind delays, and then improve bug fixing process.
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An Empirical Study on Factors
Impacting Bug Fixing Time
Feng Zhang, Foutse Khomh, Ying Zou
and Ahmed E. Hassan
2
New Assign Bug Fixing Verified Resolved
A Typical Process of Bug Fixing
3
Bug Fixing Time
New Resolved Introduced
4
Example: delay before change
Reported & Assigned
Oct-2006
Fixing started & Resolved:
Mar-2011
almost
5 years
5
Example: delay after change
Reported & Assigned
Fixing started & ended:
September-2008
Resolved:
February-2011
over
2 years
6
DBC DBF DAC
Intervals during Bug Fixing
DBR
New Resolved First
Response Bug Fixing
Start
Bug Fixing
End
Assign
DBA
Delay Before Response DBR
Delay Before Assigned DBA
Delay Before Change DBC
Duration of Bug Fixing DBF
Delay After Change DAC
7
Benefits of Studying Delays
Locate time-consuming
steps
Understand factors affecting
the delays
Improve the process of bug fixing
8
Subject Systems
3,883 logs
2,722 bugs
606 bugs
524 bugs
793 logs
638 logs
Mylyn
Eclipse Platform
PDE
9
Data Sources
Properties of bug
Change history of bug
Properties of file edits
Static metrics
History metrics
Fine grained change types
Bug ID
10
Properties of Source
Code
Properties of Code Change
Properties of Bug Report
Title
Summary
Severity of Bug
Operating System
Comments
Size (LOC, NOM)
Complexity (WMC)
Code Churn
Number of Changed Files
Fine Grained Change Types
Metrics from three Dimensions
11
RQ1: Do delays by developers exist during
bug fixing process?
RQ2: Can we characterize delays incurred by
developers before and after fixing bugs?
RQ3: What factors contribute to the delays
most?
Research Questions
12
RQ1: Bug fixing time extracted from two data sources
13
RQ1: Intervals during bug fixing process
Delay
Before
Response
Delay
Before
Assign
Delay
Before
Change
Delay
After
Change
Duration
Of
Bug Fixing
14
Enhancement v.s. Defect
364
Delay
Before
Change
56
186
4
Low Severity v.s. High Severity
198
n.s
110
n.s
RQ2: Properties of Bug Report
Delay
After
Change
Delay
Before
Change
Delay
After
Change
Delay
Before
Change
Delay
After
Change
Delay
Before
Change
Delay
After
Change
15
RQ2: Properties of Bug Report (cont’)
Length of comment
delay before change
delay after change
16
RQ2: Properties of Source Code
Size of Source Code
delay before change (sum)
delay after change (avg/sum/max)
Complexity of Source Code
delay before change (sum)
delay after change (sum/max)
17
Code Churn
delay before change (avg/sum/max)
delay after change (avg/sum/max)
Fine Grained Change Types
delay before change
delay after change
RQ2: Properties of Code Change
18
RQ3: What factors contribute to the delays most?
Events:
Long DBC : greater than median
Long DAC : greater than median
Analysis Method: (Logistic Regression Model)
Predict the probability of an event’s occurrence.
Combines different factors.
Interpretation by Coefficient:
magnitude describes importance
sign describes direction
19
RQ3: Factors Impacting Delays Before Change
-0.5
0.4
0.3
0.2
is bug with high severity
max length of comment
sum NOM
sum Code Churn
-0.5 0.5 Coefficient
20
-0.7
0.5
-0.5
max length of comment
is bug
sum WMC
max WMC
is OS Linux
length of description
avg Code Churn
-0.5
-0.3
0.3
0.7
RQ3: Factors Impacting Delays After Change
-1.0 1.0 Coefficient
21
Top Two Intervals in Bug Fixing Process
delay before and after change
Delay Before Change
level of severity, code churn (most influential factors)
Delay After Change
comments, complexity (most influential factors)
Conclusions
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