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A Human Study of Patch Maintainability. Zachary P. Fry , Bryan Landau, Westley Weimer University of Virginia {zpf5a,bal2ag,weimer}@ virginia.edu. Bug Fixing. Fixing bugs manually is difficult and costly. Recent techniques explore automated patches: Evolutionary techniques – GenProg

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a human study of patch maintainability

A Human Study of Patch Maintainability

Zachary P. Fry, Bryan Landau, Westley Weimer

University of Virginia

{zpf5a,bal2ag,weimer}@virginia.edu

bug fixing
Bug Fixing
  • Fixing bugs manually is difficult and costly.
  • Recent techniques explore automated patches:
    • Evolutionary techniques – GenProg
    • Dynamic modification – ClearView
    • Enforcement of pre/post-conditions – AutoFix-E
    • Program transformation via static analysis – AFix
  • While these techniques save developers time, there is some concern as to whether the patches produced are human-understandable and maintainable in the long run.
questions moving forward
Questions Moving Forward

How can we concretely measure these notions of human understandability and future maintainability?

Can we automatically augment machine-generated patches to improve maintainability?

In practice, are machine-generated patches as maintainable as human-generated patches?

questions moving forward1
Questions Moving Forward

How can we concretely measure these notions of human understandability and future maintainability?

Can we automatically augment machine-generated patches to improve maintainability?

In practice, are machine-generated patches as maintainable as human-generated patches?

measuring quality and maintainability
Measuring quality and maintainability
  • Functional Quality – Does the implementation match the specification?
    • Does the code execute “correctly”?
  • Non-functional Quality – Is the code understandable to humans?
    • How difficult is it to understand and alter the code in the future?

?

software functional quality
Software Functional Quality
  • Perfect:
    • Implementation matches specification
  • Direct software quality metrics:
    • Testing
    • Defect density
    • Mean time to failure
  • Indirect software quality metrics:
    • Cyclomatic complexity
    • Coupling and cohesion (CK metrics)
    • Software readability
software non functional quality
Software Non-functional Quality
  • Maintainability:
    • Human-centric factors affecting the ease with which bugs can be fixed and features can be added
    • Broadly related to the “understandability” of code
    • Not easy to concretely measure with heuristics like functional correctness
    • These automatically-generated patches have been shown to be of high quality functionally – what about non-functionally?
patch maintainability defined
Patch Maintainability Defined

Rather than using an approximation to measure understandability, we will directly measure humans’ abilities to perform maintenance tasks

Task: ask human participants questions that require them to read and understand a piece of code and measure the effort required to provide correct answers

Simulate the maintenance process as closely as possible

php bug 54454
Php Bug #54454
  • Title: “substr_compare incorrectly reports equality in some cases”
  • Bug description:
    • “if main_stris shorter than str, substr_compare [mistakenly] checks only up to the length of main_str”
  • substr_compare(“cat”, “catapult”) = true
motivating example
Motivating Example

if (offset >= s1_len) {

php_error_docref(NULL TSRMLS_CC,

E_WARNING, "The start position

cannot exceed string length");

RETURN_FALSE;

}

if (len > s1_len - offset) {

len = s1_len - offset;

}

cmp_len = (uint) (len ? len : MAX(s2_len, (s1_len - offset)));

motivating example1
Motivating Example

len--;

if (mode & 2) {

for (i = len - 1; i >= 0; i--) {

if (mask[(unsigned char)c[i]]) {

len--;

}

else {

break; }

}

}

if (return_value) {

RETVAL_STRINGL(c, len, 1);

} else {

automatic documentation
Automatic Documentation
  • Intuitions suggest that patches augmented with documentation are more maintainable
  • Human patches can contain comments with hints as to the developer’s intention when changing code
    • Automatic approaches cannot easily reason about why a change is made, but can describe what was changed
  • Automatically Synthesized Documentation:
    • DeltaDoc (Buse et al. ASE 2010)
    • Measures semantic program changes
    • Outputs natural language descriptions of changes
automatic documentation1
Automatic Documentation

if (!con->conditional_is_valid[dc->comp]) {

if (con->conf.log_condition_handling) {

TRACE("cond[%d] is valid: %d", dc->comp,

con->conditional_is_valid[dc->comp]);

}

/* If not con->conditional_is_valid[dc->comp]

No longer return COND_RESULT_UNSET; */

return COND_RESULT_UNSET;

}

/* pass the rules */

switch (dc->comp) {

case COMP_HTTP_HOST: {

char *ck_colon = NULL, *val_colon = NULL;

questions moving forward2
Questions Moving Forward

How can we concretely measure these notions of human understandability and future maintainability?

Can we automatically augment machine-generated patches to improve maintainability?

In practice, are machine-generated patches as maintainable as human-generated patches?

evaluation
Evaluation

Focused research questions to answer:

    • 1) How do different types of patches affect maintainability?
    • 2) Which source code characteristics are predictive of our maintainability measurements?
    • 3) Do participants’ intuitions about maintainability and its causes agree with measured maintainability?
  • To answer these questions directly we performed a human study using over 150 participants with real patches from existing systems
experiment subject patches
Experiment - Subject Patches
  • We used patches from six benchmarks over a variety subject domains
experiment subject patches1
Experiment - Subject Patches

Original – the defective, un-patched code used as a baseline for measuring relative changes

Human-Accepted – human patches that have not been reverted to date

Human-Reverted – human-created patches that were later reverted

Machine – automatically-generated patches created by the GenProg tool

Machine+Doc– the same patches as above, but augmented with automatically synthesized documentation

experiment maintenance task
Experiment – Maintenance Task
  • Sillitoet al. – “Questions programmers ask during software evolution tasks”
  • Recorded and categorized the questions developers actually asked while performing real maintenance tasks
    • “What is the value of the variable “y” on line X?”
  • Not: “Does this type have any siblings in the type hierarchy?”
human study
Human Study

15 if (dc->prev) {

if(con->conf.log_condition_handling) {

log_error_write(srv, __FILE__, __LINE__, "sb", "go prev", dc->prev->key);

18 }

19 /* make sure prev is checked first */

20 config_check_cond_cached(srv, con, dc->prev);

21 /* one of prev set me to FALSE */

22 if (COND_RESULT_FALSE == con->cond_cache[dc->context_ndx].result) {

23 return COND_RESULT_FALSE;

24 }

25

26 }

27

28 if (!con->conditional_is_valid[dc->comp]) {

29 if (con->conf.log_condition_handling) {

30 TRACE("cond[%d] is valid: %d", dc->comp, con->conditional_is_valid[dc->comp]);

31 }

32

return COND_RESULT_UNSET;

}

human study1
Human Study

Question presentation

Question: What is the value of the variable "con->conditional_is_valid[dc->comp]" on line 33? (recall, you can use inequality symbols in your answer)

Answer to the Question Above:

human study2
Human Study

15 if (dc->prev) {

if(con->conf.log_condition_handling) {

log_error_write(srv, __FILE__, __LINE__, "sb", "go prev", dc->prev->key);

18 }

19 /* make sure prev is checked first */

20 config_check_cond_cached(srv, con, dc->prev);

21 /* one of prev set me to FALSE */

22 if (COND_RESULT_FALSE == con->cond_cache[dc->context_ndx].result) {

23 return COND_RESULT_FALSE;

24 }

25

26 }

27

28 if (!con->conditional_is_valid[dc->comp]) {

29 if (con->conf.log_condition_handling) {

30 TRACE("cond[%d] is valid: %d", dc->comp, con->conditional_is_valid[dc->comp]);

31 }

32

return COND_RESULT_UNSET;

}

human study3
Human Study

Question presentation

Question: What is the value of the variable "con->conditional_is_valid[dc->comp]" on line 33? (recall, you can use inequality symbols in your answer)

Answer to the Question Above:

False

evaluation metrics
Evaluation Metrics
  • Correctness – is the right answer reported?
  • Time – what is the “maintenance effort” associated with understanding this code?
  • We favor correctness over time
    • Participants were instructed to spend as much time as they deemed necessary to correctly answer the questions
    • The percentages of correct answers over all types of patches were not different in a statistically significant way
    • We focus on time, as it is an analog for the software engineering effort associated with program understanding
type of patch vs maintainability
Type of Patch vs. Maintainability

Effort = average number of minutes it took participants to report a correct answer for all patches of a given type relative to the original code

type of patch vs maintainability1
Type of Patch vs. Maintainability

Effort = average number of minutes it took participants to report a correct answer for all patches of a given type relative to the original code

characteristics of maintainability
Characteristics of Maintainability
  • We measured various code features for all patches used in the human study
  • Using a logistic regression model, we can predict human accuracy when answering the questions in the study 73.16% of the time
  • A Principle Component Analysis shows that 17 features account for 90% of the variance in the data
    • Modeling maintainability is a complex problem
human intuition vs measurement
Human Intuition vs. Measurement

After completing the study, participants were asked to report which code features they thought increased maintainability the most

conclusions
Conclusions
  • From conducting a human study involving over 150 participants and patches fixing high-priority defects from real systems we conclude:
    • The fact that humans take less time, on average, to answer questions about machine-generated patches with automated documentation than human-created patches validates the possibility of using automatic patch generation techniques in practice
    • There is a strong disparity between human intuitions about maintainability and our measurements and thus we think further study is meritedin this area
modified deltadoc
Modified DeltaDoc
  • We modify DeltaDoc in the following ways:
    • Include all changes, regardless of length of output
    • Ignore all internal optimizations that lead to loss of information (e.g. ignore suspected unrelated statements)
    • Include all relevant programmatic information (e.g. function arguments)
    • Ignore all high-level output optimizations
  • Favor comprehensive explanations over brevity
  • Insert output directly above patches as comments
experiment participants
Experiment - Participants
  • Over 150 participants
    • 27 fourth-year undergraduate CS students
    • 14 CS graduate students
    • 116 Mechanical Turk internet participants
  • Accuracy cutoff imposed
    • Ensuring people don’t try to “game the system” requires special consideration
    • Any participant who failed to answer all questions or scored below one standard deviation of the average undergraduate student’s score was removed
experiment questions
Experiment - Questions
  • What conditions must hold to always reach line X during normal execution?
  • What is the value of the variable “y” on line X?
  • What conditions must be true for the function “z()” to be called on line X?
  • At line X, which variables must be in scope?
  • Given the following values for relevant variables, what lines are executed by beginning at line X? Y=5 && Z=True.