Coverage for little_loops/issue_history/debt.py: 0%

1"""Issue history technical debt analysis: cross-cutting concerns, agent effectiveness, complexity."""

2

3 from __future__ import annotations

4

5 from datetime import date, timedelta

6 from pathlib import Path

7 from typing import Any

8

9 from little_loops.issue_history._utils import get_issue_content

10 from little_loops.issue_history.models import (

11 AgentEffectivenessAnalysis,

12 AgentOutcome,

13 CompletedIssue,

14 ComplexityProxy,

15 ComplexityProxyAnalysis,

16 CrossCuttingAnalysis,

17 CrossCuttingSmell,

18 HotspotAnalysis,

19 TechnicalDebtMetrics,

20)

21 from little_loops.issue_history.parsing import (

22 _detect_processing_agent,

23 _extract_paths_from_issue,

24 _parse_resolution_action,

25)

26

27# Cross-cutting concern keywords for smell detection

28 _CROSS_CUTTING_KEYWORDS: dict[str, list[str]] = {

29 "logging": ["log", "logger", "logging", "debug", "trace", "print"],

30 "error-handling": ["error", "exception", "try", "catch", "raise", "except", "fail"],

31 "validation": ["valid", "validate", "check", "assert", "verify", "sanitize"],

32 "auth": ["auth", "permission", "role", "access", "token", "credential", "login"],

33 "caching": ["cache", "memo", "memoize", "store", "ttl", "expire", "cached"],

34}

35

36# Suggested patterns for each cross-cutting concern type

37 _CONCERN_PATTERNS: dict[str, str] = {

38 "logging": "decorator",

39 "error-handling": "middleware",

40 "validation": "decorator",

41 "auth": "middleware",

42 "caching": "decorator",

43}

44

45

46 def detect_cross_cutting_smells(

47 issues: list[CompletedIssue],

48 hotspots: HotspotAnalysis,

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

50) -> CrossCuttingAnalysis:

51 """Detect cross-cutting concerns scattered across the codebase.

52

53 Identifies when issues consistently touch multiple unrelated directories,

54 suggesting missing abstractions for cross-cutting concerns like logging,

55 error handling, or validation.

56

57 Args:

58 issues: List of completed issues

59 hotspots: Hotspot analysis results (provides directory reference)

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

61

62 Returns:

63 CrossCuttingAnalysis with detected smells

64 """

65 if not issues:

66 return CrossCuttingAnalysis()

67

68 # Track concern data: {concern_type: {dirs: set, issues: list}}

69 concern_data: dict[str, dict[str, Any]] = {}

70 for concern_type in _CROSS_CUTTING_KEYWORDS:

71 concern_data[concern_type] = {

72 "directories": set(),

73 "issue_ids": [],

74 }

75

76 # Get all unique directories from hotspots for scatter score calculation

77 all_directories: set[str] = set()

78 if hotspots.directory_hotspots:

79 all_directories = {h.path for h in hotspots.directory_hotspots}

80

81 # Analyze each issue

82 for issue in issues:

83 content = get_issue_content(issue, contents)

84 if content is None:

85 continue

86 content_lower = content.lower()

87

88 # Extract paths from this issue

89 paths = _extract_paths_from_issue(content)

90 issue_dirs = {str(Path(p).parent) for p in paths if "/" in p or "\\" in p}

91 all_directories.update(issue_dirs)

92

93 # Check if this issue touches multiple directories (3+)

94 if len(issue_dirs) < 3:

95 continue

96

97 # Check for concern keywords

98 for concern_type, keywords in _CROSS_CUTTING_KEYWORDS.items():

99 if any(kw in content_lower for kw in keywords):

100 concern_data[concern_type]["directories"].update(issue_dirs)

101 if issue.issue_id not in concern_data[concern_type]["issue_ids"]:

102 concern_data[concern_type]["issue_ids"].append(issue.issue_id)

103

104 # Build CrossCuttingSmell objects

105 smells: list[CrossCuttingSmell] = []

106 total_dirs = len(all_directories) if all_directories else 1

107

108 for concern_type, data in concern_data.items():

109 if data["issue_ids"]: # Only include concerns with detected issues

110 dirs = sorted(data["directories"])

111 scatter_score = len(dirs) / total_dirs if total_dirs > 0 else 0.0

112

113 smell = CrossCuttingSmell(

114 concern_type=concern_type,

115 affected_directories=dirs,

116 issue_count=len(data["issue_ids"]),

117 issue_ids=data["issue_ids"],

118 scatter_score=scatter_score,

119 suggested_pattern=_CONCERN_PATTERNS.get(concern_type, "aspect"),

120 )

121 smells.append(smell)

122

123 # Sort by scatter score descending

124 smells.sort(key=lambda s: -s.scatter_score)

125

126 # Identify most scattered concern

127 most_scattered = smells[0].concern_type if smells else ""

128

129 # Build consolidation opportunities

130 consolidation_opportunities = []

131 for smell in smells:

132 if smell.scatter_score >= 0.3: # Threshold for suggesting consolidation

133 consolidation_opportunities.append(

134 f"Centralize {smell.concern_type} ({smell.issue_count} issues would benefit)"

135 )

136

137 return CrossCuttingAnalysis(

138 smells=smells,

139 most_scattered_concern=most_scattered,

140 consolidation_opportunities=consolidation_opportunities[:10],

141 )

142

143

144 def analyze_agent_effectiveness(

145 issues: list[CompletedIssue],

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

147) -> AgentEffectivenessAnalysis:

148 """Analyze agent effectiveness across issue types.

149

150 Groups issues by processing agent and issue type, calculating

151 success/failure/rejection rates for each combination.

152

153 Args:

154 issues: List of completed issues

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

156

157 Returns:

158 AgentEffectivenessAnalysis with outcomes and recommendations

159 """

160 if not issues:

161 return AgentEffectivenessAnalysis()

162

163 # Track outcomes by (agent, issue_type)

164 outcomes_map: dict[tuple[str, str], AgentOutcome] = {}

165

166 for issue in issues:

167 content = get_issue_content(issue, contents)

168 if content is None:

169 continue

170

171 # Detect agent (discovered_by may contain source info in some cases)

172 agent = _detect_processing_agent(content, issue.discovered_by)

173

174 # Get resolution outcome

175 resolution = _parse_resolution_action(content)

176

177 # Get or create outcome tracker

178 key = (agent, issue.issue_type)

179 if key not in outcomes_map:

180 outcomes_map[key] = AgentOutcome(

181 agent_name=agent,

182 issue_type=issue.issue_type,

183 )

184

185 outcome = outcomes_map[key]

186

187 # Categorize outcome

188 if resolution == "completed":

189 outcome.success_count += 1

190 elif resolution in ("rejected", "invalid", "duplicate"):

191 outcome.rejection_count += 1

192 else: # deferred or other

193 outcome.failure_count += 1

194

195 # Build outcomes list

196 outcomes = list(outcomes_map.values())

197

198 # Determine best agent per issue type

199 best_agent_by_type: dict[str, str] = {}

200 type_agents: dict[str, list[AgentOutcome]] = {}

201

202 for outcome in outcomes:

203 if outcome.issue_type not in type_agents:

204 type_agents[outcome.issue_type] = []

205 type_agents[outcome.issue_type].append(outcome)

206

207 for issue_type, agent_outcomes in type_agents.items():

208 # Require minimum sample size

209 significant_outcomes = [o for o in agent_outcomes if o.total_count >= 3]

210 if significant_outcomes:

211 best = max(significant_outcomes, key=lambda o: o.success_rate)

212 best_agent_by_type[issue_type] = best.agent_name

213

214 # Identify problematic combinations (success rate < 50% with >= 5 samples)

215 problematic_combinations: list[tuple[str, str, str]] = []

216 for outcome in outcomes:

217 if outcome.total_count >= 5 and outcome.success_rate < 0.5:

218 reason = (

219 f"{outcome.success_rate * 100:.0f}% success "

220 f"({outcome.success_count}/{outcome.total_count})"

221 )

222 problematic_combinations.append((outcome.agent_name, outcome.issue_type, reason))

223

224 # Sort by success rate ascending (worst first)

225 problematic_combinations.sort(key=lambda x: float(x[2].split("%")[0]))

226

227 return AgentEffectivenessAnalysis(

228 outcomes=sorted(outcomes, key=lambda o: (o.agent_name, o.issue_type)),

229 best_agent_by_type=best_agent_by_type,

230 problematic_combinations=problematic_combinations,

231 )

232

233

234 def analyze_complexity_proxy(

235 issues: list[CompletedIssue],

236 hotspots: HotspotAnalysis,

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

238) -> ComplexityProxyAnalysis:

239 """Use issue duration as proxy for code complexity.

240

241 Areas that consistently take longer to resolve suggest higher complexity,

242 insufficient documentation, or accumulated technical debt.

243

244 Args:

245 issues: List of completed issues with dates

246 hotspots: Pre-computed hotspot analysis for path information

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

248

249 Returns:

250 ComplexityProxyAnalysis with duration-based complexity metrics

251 """

252 # Calculate durations for all issues with both dates

253 issue_durations: dict[str, float] = {} # issue_id -> days

254 for issue in issues:

255 if issue.discovered_date and issue.completed_date:

256 delta = issue.completed_date - issue.discovered_date

257 days = float(delta.days)

258 if days >= 0: # Sanity check

259 issue_durations[issue.issue_id] = days

260

261 if not issue_durations:

262 return ComplexityProxyAnalysis()

263

264 # Calculate baseline (median duration)

265 all_durations = sorted(issue_durations.values())

266 n = len(all_durations)

267 if n % 2 == 0:

268 baseline_days = (all_durations[n // 2 - 1] + all_durations[n // 2]) / 2

269 else:

270 baseline_days = all_durations[n // 2]

271

272 if baseline_days == 0:

273 baseline_days = 1.0 # Avoid division by zero

274

275 # Map issues to their affected files by reading issue content

276 issue_to_files: dict[str, list[str]] = {}

277 for issue in issues:

278 if issue.issue_id in issue_durations:

279 content = get_issue_content(issue, contents)

280 if content is None:

281 continue

282 paths = _extract_paths_from_issue(content)

283 if paths:

284 issue_to_files[issue.issue_id] = paths

285

286 # Aggregate durations by file

287 file_durations: dict[str, list[tuple[str, float]]] = {} # path -> [(issue_id, days), ...]

288 for issue_id, files in issue_to_files.items():

289 days = issue_durations[issue_id]

290 for f in files:

291 if f not in file_durations:

292 file_durations[f] = []

293 file_durations[f].append((issue_id, days))

294

295 # Aggregate durations by directory

296 dir_durations: dict[str, list[tuple[str, float]]] = {}

297 for path, entries in file_durations.items():

298 dir_path = "/".join(path.split("/")[:-1]) + "/" if "/" in path else "./"

299 if dir_path not in dir_durations:

300 dir_durations[dir_path] = []

301 dir_durations[dir_path].extend(entries)

302

303 # Build file complexity proxies

304 file_complexity: list[ComplexityProxy] = []

305 for path, entries in file_durations.items():

306 if len(entries) < 2: # Need at least 2 data points

307 continue

308

309 durations = [d for _, d in entries]

310 avg = sum(durations) / len(durations)

311 sorted_d = sorted(durations)

312 median = sorted_d[len(sorted_d) // 2]

313 slowest = max(entries, key=lambda x: x[1])

314

315 # Normalize complexity score (0-1 based on how much slower than baseline)

316 ratio = avg / baseline_days

317 complexity_score = min(1.0, (ratio - 1) / 4) # 5x slower = 1.0

318 complexity_score = max(0.0, complexity_score)

319

320 comparison = f"{ratio:.1f}x baseline" if ratio >= 1.5 else "near baseline"

321

322 file_complexity.append(

323 ComplexityProxy(

324 path=path,

325 avg_resolution_days=avg,

326 median_resolution_days=median,

327 issue_count=len(entries),

328 slowest_issue=slowest,

329 complexity_score=complexity_score,

330 comparison_to_baseline=comparison,

331 )

332 )

333

334 # Build directory complexity proxies

335 directory_complexity: list[ComplexityProxy] = []

336 for dir_path, entries in dir_durations.items():

337 if len(entries) < 3: # Need at least 3 data points for directories

338 continue

339

340 # Deduplicate by issue_id for directory-level stats

341 unique_entries: dict[str, float] = {}

342 for issue_id, days in entries:

343 if issue_id not in unique_entries or days > unique_entries[issue_id]:

344 unique_entries[issue_id] = days

345

346 entries_list = list(unique_entries.items())

347 durations = list(unique_entries.values())

348 avg = sum(durations) / len(durations)

349 sorted_d = sorted(durations)

350 median = sorted_d[len(sorted_d) // 2]

351 slowest = max(entries_list, key=lambda x: x[1])

352

353 ratio = avg / baseline_days

354 complexity_score = min(1.0, (ratio - 1) / 4)

355 complexity_score = max(0.0, complexity_score)

356

357 comparison = f"{ratio:.1f}x baseline" if ratio >= 1.5 else "near baseline"

358

359 directory_complexity.append(

360 ComplexityProxy(

361 path=dir_path,

362 avg_resolution_days=avg,

363 median_resolution_days=median,

364 issue_count=len(unique_entries),

365 slowest_issue=slowest,

366 complexity_score=complexity_score,

367 comparison_to_baseline=comparison,

368 )

369 )

370

371 # Sort by complexity score descending

372 file_complexity.sort(key=lambda c: -c.complexity_score)

373 directory_complexity.sort(key=lambda c: -c.complexity_score)

374

375 # Identify outliers (>2x baseline)

376 complexity_outliers = [

377 c.path for c in file_complexity if c.avg_resolution_days > baseline_days * 2

378 ]

379

380 return ComplexityProxyAnalysis(

381 file_complexity=file_complexity[:10],

382 directory_complexity=directory_complexity[:10],

383 baseline_days=baseline_days,

384 complexity_outliers=complexity_outliers[:10],

385 )

386

387

388 def _calculate_debt_metrics(

389 completed_issues: list[CompletedIssue],

390 active_issues: list[tuple[Path, str, str, date | None]],

391) -> TechnicalDebtMetrics:

392 """Calculate technical debt health metrics.

393

394 Args:

395 completed_issues: List of completed issues

396 active_issues: List of active issue tuples

397

398 Returns:

399 TechnicalDebtMetrics with calculated values

400 """

401 today = date.today()

402 metrics = TechnicalDebtMetrics()

403

404 # Backlog size

405 metrics.backlog_size = len(active_issues)

406

407 # Count aging and high priority

408 for _path, _issue_type, priority, discovered_date in active_issues:

409 if priority in ("P0", "P1"):

410 metrics.high_priority_open += 1

411

412 if discovered_date:

413 age = (today - discovered_date).days

414 if age >= 30:

415 metrics.aging_30_plus += 1

416 if age >= 60:

417 metrics.aging_60_plus += 1

418

419 # Calculate backlog growth rate (issues per week)

420 # Look at last 4 weeks of completions vs creations

421 four_weeks_ago = today - timedelta(days=28)

422

423 completed_recently = sum(

424 1 for i in completed_issues if i.completed_date and i.completed_date >= four_weeks_ago

425 )

426

427 created_recently = sum(1 for _, _, _, d in active_issues if d and d >= four_weeks_ago)

428

429 # Net change per week

430 if completed_recently > 0 or created_recently > 0:

431 metrics.backlog_growth_rate = (created_recently - completed_recently) / 4.0

432

433 # Debt paydown ratio (bug fixes vs features)

434 bug_count = sum(1 for i in completed_issues if i.issue_type == "BUG")

435 feat_count = sum(1 for i in completed_issues if i.issue_type == "FEAT")

436

437 if feat_count > 0:

438 metrics.debt_paydown_ratio = bug_count / feat_count

439 elif bug_count > 0:

440 metrics.debt_paydown_ratio = float(bug_count) # All maintenance

441

442 return metrics