1"""Issue history regression clustering analysis."""

2

3 from __future__ import annotations

4

5 from pathlib import Path

6

7 from little_loops.issue_history._utils import get_issue_content

8 from little_loops.issue_history.models import (

9 CompletedIssue,

10 RegressionAnalysis,

11 RegressionCluster,

12)

13 from little_loops.issue_history.parsing import _extract_paths_from_issue

14

15

16 def analyze_regression_clustering(

17 issues: list[CompletedIssue],

18 contents: dict[Path, str] | None = None,

19) -> RegressionAnalysis:

20 """Detect files where bug fixes frequently lead to new bugs.

21

22 Uses heuristics:

23 1. Temporal proximity: Bug B completed within 7 days of Bug A

24 2. File overlap: Both bugs affect same file(s)

25

26 Args:

27 issues: List of completed issues

28 contents: Pre-loaded issue file contents (path -> content)

29

30 Returns:

31 RegressionAnalysis with clusters of related regressions

32 """

33 # Filter to bugs only and sort by completion date

34 bugs = [i for i in issues if i.issue_type == "BUG" and i.completed_date]

35 bugs.sort(key=lambda i: i.completed_date) # type: ignore

36

37 if len(bugs) < 2:

38 return RegressionAnalysis()

39

40 # Extract file paths for each bug

41 bug_files: dict[str, set[str]] = {} # issue_id -> set of files

42 for bug in bugs:

43 content = get_issue_content(bug, contents)

44 if content is None:

45 bug_files[bug.issue_id] = set()

46 else:

47 bug_files[bug.issue_id] = set(_extract_paths_from_issue(content))

48

49 # Find regression pairs (temporal proximity + file overlap)

50 regression_pairs: list[tuple[CompletedIssue, CompletedIssue, set[str]]] = []

51

52 for i, bug_a in enumerate(bugs[:-1]):

53 files_a = bug_files.get(bug_a.issue_id, set())

54 if not files_a:

55 continue

56

57 for bug_b in bugs[i + 1 :]:

58 # Check temporal proximity (within 7 days)

59 days_apart = (bug_b.completed_date - bug_a.completed_date).days # type: ignore

60 if days_apart > 7:

61 break # Bugs are sorted, no need to check further

62

63 files_b = bug_files.get(bug_b.issue_id, set())

64 if not files_b:

65 continue

66

67 # Check file overlap

68 overlap = files_a & files_b

69 if overlap:

70 regression_pairs.append((bug_a, bug_b, overlap))

71

72 if not regression_pairs:

73 return RegressionAnalysis()

74

75 # Group by primary file (most common overlapping file)

76 file_regressions: dict[str, list[tuple[str, str, int]]] = {} # file -> [(id_a, id_b, days)]

77

78 for bug_a, bug_b, overlap in regression_pairs:

79 days = (bug_b.completed_date - bug_a.completed_date).days # type: ignore

80 for file_path in overlap:

81 if file_path not in file_regressions:

82 file_regressions[file_path] = []

83 file_regressions[file_path].append((bug_a.issue_id, bug_b.issue_id, days))

84

85 # Build clusters

86 clusters: list[RegressionCluster] = []

87

88 for file_path, pairs in file_regressions.items():

89 # Determine time pattern

90 avg_days = sum(d for _, _, d in pairs) / len(pairs)

91 if avg_days < 3:

92 time_pattern = "immediate"

93 elif len(pairs) >= 3:

94 time_pattern = "chronic"

95 else:

96 time_pattern = "delayed"

97

98 # Determine severity

99 if len(pairs) >= 4:

100 severity = "critical"

101 elif len(pairs) >= 2:

102 severity = "high"

103 else:

104 severity = "medium"

105

106 # Collect related files

107 related_files: set[str] = set()

108 for bug_a, bug_b, _ in regression_pairs:

109 if file_path in (

110 bug_files.get(bug_a.issue_id, set()) & bug_files.get(bug_b.issue_id, set())

111 ):

112 related_files.update(bug_files.get(bug_a.issue_id, set()))

113 related_files.update(bug_files.get(bug_b.issue_id, set()))

114 related_files.discard(file_path)

115

116 clusters.append(

117 RegressionCluster(

118 primary_file=file_path,

119 regression_count=len(pairs),

120 fix_bug_pairs=[(a, b) for a, b, _ in pairs],

121 related_files=sorted(related_files),

122 time_pattern=time_pattern,

123 severity=severity,

124 )

125 )

126

127 # Sort by regression count descending

128 clusters.sort(key=lambda c: (-c.regression_count, c.primary_file))

129

130 # Identify most fragile files

131 most_fragile = [c.primary_file for c in clusters[:5]]

132

133 return RegressionAnalysis(

134 clusters=clusters[:10], # Top 10

135 total_regression_chains=len(regression_pairs),

136 most_fragile_files=most_fragile,

137 )