A Human Study of Patch Maintainability

1. A Human Study of Patch Maintainability Zachary P. Fry, Bryan Landau, Westley Weimer University of Virginia {zpf5a,bal2ag,weimer}@virginia.edu

2. 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.

3. 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?

4. 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?

5. 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? ✓ ?

6. 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

7. 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?

8. 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

9. 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

10. 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)));

11. 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 {

12. 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

13. 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;

14. 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?

15. 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

16. Experiment - Subject Patches • We used patches from six benchmarks over a variety subject domains

17. 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

18. 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?”

19. 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; } …

20. 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:

21. 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; } …

22. 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

23. 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

24. 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

25. 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

26. 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

27. Characteristics of Maintainability

28. Human Intuition vs. Measurement After completing the study, participants were asked to report which code features they thought increased maintainability the most

29. 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

30. Questions?

31. 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

32. 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

33. 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.