Welcome to Reading’s show! Instructor: Nguyen Ngoc Vu Students: Van Anh - Quy Thanh Class 3C04.
Presented by Vu Nguyen on behalf of Vu Nguyen, Barry Boehm, Phongphan Danphitsanuphan
-
Upload
abdul-sawyer -
Category
Documents
-
view
21 -
download
2
description
Transcript of Presented by Vu Nguyen on behalf of Vu Nguyen, Barry Boehm, Phongphan Danphitsanuphan
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 1
Assessing and Estimating Corrective, Enhancive, and Reductive Maintenance
Tasks: A Controlled Experiment *
Presented by Vu Nguyen
on behalf of
Vu Nguyen, Barry Boehm, Phongphan Danphitsanuphan
(*) paper accepted for Asia-Pacific Software Engineering Conference 2009 (APSEC 2009)
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 2
Outline
Motivation and Background
Experiment Design
Results and Explanatory Models
Conclusions
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 3
Motivation and Background
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 4
Maintenance is crucial in software engineering
• Systems are tightly coupled with their environment
– Environment changes require changing its software systems
• Technologies and requirements are continuously changing
– Software systems are outdated quickly
– Software systems must be updated and upgraded to maintain their values
• Maintenance is important in market competition
– New software has more advantages than the existing one
– Software system must be upgraded to keep its market share
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 5
Majority of software costs incur after the first operational release (Boehm ’81)
• Maintenance cost is usually 2x to 100x as much as new development cost (Sommerville, 2006)
K$
Time
100 200 300
System 1
System 2
Release 1 Release 2 Release 3
Release 1 Release 2
Adapted from (Summerville, 2006)New Development
Maintenance
Release N
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 6
Software estimation community has paid little attention to software maintenance
• Most estimation models regard maintenance estimation as secondary
– COCOMO, SEER-SEM, SLIM, PRICE-S models were built using mainly data of new development projects
• Use of SLOC metrics for models is inconsistent
– Some models use SLOC added, modified, deleted
– Others use only SLOC added and modified
• Impact of different SLOC metrics on productivity has not been investigated
Projects tend to use experience or expert judgment methods to estimate software effort and cost instead
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 7
Research Questions and Hypotheses
• RQ1: Are there any differences in the productivity of enhancive, corrective, and deductive maintenance?
– Hypothesis 1 (H1): no difference
• RQ2: Are there any differences in the effort distribution among the maintenance types?
– Hypothesis 2 (H2): no difference
Explanatory models to estimate participant’s maintenance effort
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 8
Software Maintenance
• Software Maintenance
– “Modification of a software product after delivery to correct faults, to improve performance or other attributes, or to adapt the product to a modified environment” [IEEE ‘98]
• Types of Maintenance
– Swanson ’76: Adaptive, Corrective, Perfective
– IEEE ’98: all Swanson’s plus Preventive
– Chapin et al, 2001: 12 types, including three business rules types:• Enhancive• Corrective• Reductive
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 9
Experimental Design
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 10
Experiment DescriptionTask Group # Tasks # Participants
Enhancive 5 7
Corrective 6 9
Reductive 6 8
• 23 masters’ students and 1 senior, computer science major
• Participants worked on tasks individually in the lab
– Enhancive: add new capabilities
– Corrective: fix the existing capabilities
– Reductive: remove the existing capabilities
• UCC as a target program
– 5K+ source statements (logical SLOC) in 20 C++ classes
• MS Visual Studio 2005 was used for maintenance
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 11
Calculating Maintenance SLOC
Equivalent SLOC = TRCF x AAM
1,100
1,])1(1[1( 2
AAFforUNFMxSU
AAF
AAFforUNFMxSUxAAFAAFAAM
TRCF
SAAF
TRCF = the total SLOC of task-relevant code fragments
S = the size in SLOC (added, modified, or deleted)
SU = the software understandability
UNFM = the level of programmer unfamiliarity with the program
Task-relevant code fragment
(TRCF)
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 12
Results and Models
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 13
Resulted Data
• 24 students participated
• Each task requires four activities
– Task comprehension
– Code isolation
– Editing code
– Unit test
• Timesheet has 490 activity records, totaling 77.02 hours
• Total of 909 SLOC added, modified, and deleted
402 added
216 modified
291 deleted
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 14
Effort distribution is different among the groups
• Corrective group spent much time for code isolation
– twice as much as that of the enhancive group
• Enhancive group spent majority of time for editing code
• Effort distribution is statistically different among three groups (p-value = 0.0013)
– H2 is rejectedKruskal-Wallis rank-sum test
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 15
Productivity is significantly different among the groups
• Corrective group has lowest productivity
• Reductive group has highest productivity
• Productivity between groups are statistically different (p-value = 0.0004)
– H1 is rejectedEnhancive Reductive Corrective
Pro
du
ctiv
ity
(SL
OC
/Hou
r)
40 30 20 10
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 16
Participant Time Explanatory Models
• Two models for participant effort
M1 Effort = 78.1 + 2.2 * S * EAF
M2 Effort = 43.9 + (2.8*Add + 5.3*Mod + 1.3*Del) * EAF
Effort = Time spent by the participant on all maintenance tasks
EAF = Effort adjustment factor, a product of Programmer capability (PCAP), Language experience (LTEX), and Platform Experience (PLEX)
Add, Mod, Del = Equivalent SLOC added, modified, and deleted by the participant, respectively
S = Add + Mod + Del
All the estimates of coefficients are significant (p-value < 0.05)
R2 = 0.5 R2 = 0.75
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 17
Model M2 outperforms M1
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Participant #
MR
E
M1 M2
M1 M2
MMRE 33% 20%
PRED(.25) 46% 71%
PRED(.3) 58% 79%
i
iii Actual
EstimateActualMRE
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 18
Threats to Validity
• Internal design
– Groups have imbalanced skills and capability
– Imbalanced complexity of tasks on three groups
– Incorrect time recorded
• Generalizability
– Professional programmers are more experienced
– Professional programmers are more familiar with the software maintained
– Maintenance process is different in industry
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 19
Conclusions
• Productivity and effort distribution are significantly different among the maintenance types
• SLOC metrics are relevant factors for estimating effort
• Three SLOC metrics (added, modified, and deleted) have different impact on effort
– SLOC deleted is an important factor for estimating effort
– It is more expensive to modify than to add or delete a statement
• Assigning experienced programmers to fixing defect can save up to 40% of effort
University of Southern California
Center for Systems and Software Engineering
© 2009, USC-CSSE 20
References
• Barry W. Boehm, “Software Engineering Economics”, Prentice Hall, 1991
• Ian Sommerville, “Software Engineering,” 8th Ed., Addison-Wesley, 2006
• Ned Chapin, et al., “Types of software evolution and software maintenance,” Journal of Software Maintenance: Research and Practice, v.13 n.1, p.3-30, Jan. 2001
• IEEE, IEEE Standard Glossary of Software Engineering Terminology. Institute of Electrical and Electronics Engineers: New York NY, 1990: 83 pp
• IEEE Std 1219-1998, IEEE Standard for Software Maintenance, IEEE Computer Society, 1998