scan-v5.json (27441B)
1 { 2 "scan_version": 5, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generation", 6 "authors": [ 7 "Qinghua Xu", 8 "Guancheng Wang", 9 "Lionel C. Briand", 10 "Kui Liu" 11 ], 12 "year": 2025, 13 "venue": "arXiv", 14 "arxiv_id": "2506.02943", 15 "doi": null 16 }, 17 "checklist": { 18 "claims_and_evidence": { 19 "abstract_claims_supported": { 20 "applies": true, 21 "answer": true, 22 "justification": "All abstract claims (comparable coverage to EvoSuite, superior mutation score, ≥21.1pp gain over TOGLL in oracle correctness) are directly supported by Table 1 and Figure 4 with statistical testing.", 23 "source": "haiku" 24 }, 25 "causal_claims_justified": { 26 "applies": true, 27 "answer": true, 28 "justification": "Ablation studies in Section 5.3 systematically remove each key component (Planner, Requirement Engineer, panel discussion) with Wilcoxon significance tests to support causal claims about each component's contribution.", 29 "source": "haiku" 30 }, 31 "generalization_bounded": { 32 "applies": true, 33 "answer": true, 34 "justification": "The paper explicitly limits scope to Java methods without external or user-defined class dependencies, excludes Defects4J and SF110, and acknowledges external validity threats in Section 6.2.", 35 "source": "haiku" 36 }, 37 "alternative_explanations_discussed": { 38 "applies": true, 39 "answer": false, 40 "justification": "The paper attributes CANDOR's mutation score advantage to LLM semantic understanding without considering alternatives such as prompt engineering artifacts or dataset-specific patterns favoring LLM outputs.", 41 "source": "haiku" 42 }, 43 "proxy_outcome_distinction": { 44 "applies": true, 45 "answer": true, 46 "justification": "The paper explicitly states mutation score is 'a proxy for bug-finding capability' (Section 6.2) and notes real bug detection on Defects4J was outside scope.", 47 "source": "haiku" 48 } 49 }, 50 "limitations_and_scope": { 51 "limitations_section_present": { 52 "applies": true, 53 "answer": true, 54 "justification": "Section 6.2 'Threats to Validity' covers construct, internal, external, and conclusion validity with dedicated discussion of specific threats.", 55 "source": "haiku" 56 }, 57 "threats_to_validity_specific": { 58 "applies": true, 59 "answer": true, 60 "justification": "Specific threats named include: scope limited to methods without external dependencies, LLM choice affecting results, data leakage from HumanEvalJava being in pretraining data, and only two benchmark datasets.", 61 "source": "haiku" 62 }, 63 "scope_boundaries_stated": { 64 "applies": true, 65 "answer": true, 66 "justification": "The paper explicitly states CANDOR only handles Java methods without user-defined or external class dependencies and acknowledges this excludes Defects4J and SF110.", 67 "source": "haiku" 68 } 69 }, 70 "conflicts_of_interest": { 71 "funding_disclosed": { 72 "applies": true, 73 "answer": false, 74 "justification": "No funding disclosure or acknowledgment section appears in the paper text despite affiliation with Research Ireland Lero Centre and Huawei.", 75 "source": "haiku" 76 }, 77 "affiliations_disclosed": { 78 "applies": true, 79 "answer": true, 80 "justification": "All author affiliations are clearly disclosed: Research Ireland Lero Centre/University of Limerick, University of Ottawa, and Huawei Software Engineering Application Technology Lab.", 81 "source": "haiku" 82 }, 83 "funder_independent_of_outcome": { 84 "applies": true, 85 "answer": false, 86 "justification": "Co-author Kui Liu is affiliated with Huawei, which has direct commercial interests in automated software testing tools; no independence statement is provided.", 87 "source": "haiku" 88 }, 89 "financial_interests_declared": { 90 "applies": true, 91 "answer": false, 92 "justification": "No competing interests or financial interests declaration is present anywhere in the paper.", 93 "source": "haiku" 94 } 95 }, 96 "scope_and_framing": { 97 "key_terms_defined": { 98 "applies": true, 99 "answer": true, 100 "justification": "Key terms are defined with citations: test prefix, test oracle, regression oracle, specification-based oracle, mutation score, oracle correctness, and cyclomatic complexity are all explicitly defined.", 101 "source": "haiku" 102 }, 103 "intended_contribution_clear": { 104 "applies": true, 105 "answer": true, 106 "justification": "Four explicit contributions are enumerated in the introduction: first multi-agent Java end-to-end test framework, panel discussion strategy, dual-LLM pipeline, and experimental validation.", 107 "source": "haiku" 108 }, 109 "engagement_with_prior_work": { 110 "applies": true, 111 "answer": true, 112 "justification": "Section 7 provides detailed related work on both test prefix and oracle generation, explicitly positioning CANDOR relative to EvoSuite, TOGLL, LLM-Empirical, TOGA, and other approaches.", 113 "source": "haiku" 114 } 115 } 116 }, 117 "type_checklist": { 118 "empirical": { 119 "artifacts": { 120 "code_released": { 121 "applies": true, 122 "answer": false, 123 "justification": "Section 4.4 states 'we plan to release the code publicly upon paper acceptance' — a promise of future release, not actual release.", 124 "source": "haiku" 125 }, 126 "data_released": { 127 "applies": true, 128 "answer": false, 129 "justification": "HumanEvalJava is a public benchmark but LeetCodeJava (the novel contribution dataset) is not formally packaged and released; raw experimental outputs are unavailable.", 130 "source": "haiku" 131 }, 132 "environment_specified": { 133 "applies": true, 134 "answer": false, 135 "justification": "Hardware specs are given (Precision 7960 Tower, dual RTX 6000 Ada GPUs) and LangChain is mentioned, but no requirements.txt, Dockerfile, or full dependency specification is provided.", 136 "source": "haiku" 137 }, 138 "reproduction_instructions": { 139 "applies": true, 140 "answer": false, 141 "justification": "No step-by-step reproduction instructions exist; with code unreleased, independent reproduction is not feasible from the paper alone.", 142 "source": "haiku" 143 } 144 }, 145 "statistical_methodology": { 146 "confidence_intervals_or_error_bars": { 147 "applies": true, 148 "answer": false, 149 "justification": "Results are reported as averages over 3 runs across all tables and figures; no confidence intervals or error bars are provided.", 150 "source": "haiku" 151 }, 152 "significance_tests": { 153 "applies": true, 154 "answer": true, 155 "justification": "Wilcoxon Signed Rank tests are applied throughout all RQs with significance level 0.05, as stated in Section 4.3.", 156 "source": "haiku" 157 }, 158 "effect_sizes_reported": { 159 "applies": true, 160 "answer": true, 161 "justification": "Vargha and Delaney's A12 effect size is reported for EVO-CANDOR vs TOGLL comparisons (A12=0.920 on correct code, A12=0.960 on faulty code).", 162 "source": "haiku" 163 }, 164 "sample_size_justified": { 165 "applies": true, 166 "answer": false, 167 "justification": "LeetCodeJava sample of 100 methods is justified only by 'time and computational resource constraints' with no power analysis or principled sample size calculation.", 168 "source": "haiku" 169 }, 170 "variance_reported": { 171 "applies": true, 172 "answer": false, 173 "justification": "Experiments are repeated 3 times and averages reported, but no standard deviations or variance measures appear in any result table or figure.", 174 "source": "haiku" 175 } 176 }, 177 "evaluation_design": { 178 "baselines_included": { 179 "applies": true, 180 "answer": true, 181 "justification": "Four baselines evaluated: EvoSuite, LLM-Empirical, TOGLL, and the EVO-CANDOR variant designed for fair oracle comparison.", 182 "source": "haiku" 183 }, 184 "baselines_contemporary": { 185 "applies": true, 186 "answer": true, 187 "justification": "TOGLL (2024) is the acknowledged SOTA for oracle generation; EvoSuite is acknowledged as unmaintained since 2021 but remains the SOTA for coverage per Tang et al. 2024.", 188 "source": "haiku" 189 }, 190 "ablation_study": { 191 "applies": true, 192 "answer": true, 193 "justification": "Section 5.3 ablates three components (w/o Planner, w/o Requirement Engineer, w/o Panel, plus w/ Voting variant) with statistical significance testing.", 194 "source": "haiku" 195 }, 196 "multiple_metrics": { 197 "applies": true, 198 "answer": true, 199 "justification": "Four metrics used: line coverage, branch coverage, mutation score, and oracle correctness, measured independently.", 200 "source": "haiku" 201 }, 202 "human_evaluation": { 203 "applies": false, 204 "answer": false, 205 "justification": "Human evaluation is not applicable; test quality is assessed via automated metrics (coverage, mutation killing, oracle correctness against known-correct implementations).", 206 "source": "haiku" 207 }, 208 "held_out_test_set": { 209 "applies": true, 210 "answer": true, 211 "justification": "Two dedicated evaluation datasets used (HumanEvalJava: 160 programs, LeetCodeJava: 100 programs); no training is performed — prompt engineering only.", 212 "source": "haiku" 213 }, 214 "per_category_breakdown": { 215 "applies": true, 216 "answer": true, 217 "justification": "Results broken down by dataset (HumanEvalJava, Leetcode-Medium, Leetcode-Hard) and by condition (correct vs. faulty source code) throughout Tables 1-2 and Figure 4.", 218 "source": "haiku" 219 }, 220 "failure_cases_discussed": { 221 "applies": true, 222 "answer": true, 223 "justification": "Section 6.1 discusses specific failure cases, including Panelists hallucinating max_element() as minimum, and over 70% of cases showing Panelist disagreements that required Curator correction.", 224 "source": "haiku" 225 }, 226 "negative_results_reported": { 227 "applies": true, 228 "answer": true, 229 "justification": "The paper reports that Requirement Engineer removal was not significant on Leetcode-Medium (p=0.17), and EvoSuite achieves slightly higher branch coverage on Leetcode-Medium than CANDOR.", 230 "source": "haiku" 231 } 232 }, 233 "setup_transparency": { 234 "model_versions_specified": { 235 "applies": true, 236 "answer": true, 237 "justification": "Specific model names provided: 'Llama 3.1 70B' as basic LLM and 'DeepSeek R1 Llama-distilled 70B' as reasoning LLM, with alternatives (CodeLlama 70B, Mistral 22B) reported in appendix.", 238 "source": "haiku" 239 }, 240 "prompts_provided": { 241 "applies": true, 242 "answer": true, 243 "justification": "Figure 3 provides full system prompts and user prompts for all 8 agents, with variable placeholders (e.g., {{source_code}}) explicitly labeled.", 244 "source": "haiku" 245 }, 246 "hyperparameters_reported": { 247 "applies": true, 248 "answer": true, 249 "justification": "Key hyperparameters reported: max_attempts=3, number of Panelist pipelines=3, DeepSeek output token limit=2000, EvoSuite assertion_timeout=2min, and pipeline selection rationale given.", 250 "source": "haiku" 251 }, 252 "scaffolding_described": { 253 "applies": true, 254 "answer": true, 255 "justification": "Section 3 describes all three pipeline steps and all 8 specialized agents in detail, including their roles, inputs, outputs, and interaction flow.", 256 "source": "haiku" 257 }, 258 "data_preprocessing_documented": { 259 "applies": true, 260 "answer": true, 261 "justification": "LeetCodeJava construction described (random sample from LeetCode medium/hard, community-maintained solutions from doocs/leetcode); dataset statistics (LOC, CC) reported.", 262 "source": "haiku" 263 } 264 }, 265 "data_integrity": { 266 "raw_data_available": { 267 "applies": true, 268 "answer": false, 269 "justification": "Generated test files, mutation results, and oracle evaluations are not released; code release is deferred to post-acceptance.", 270 "source": "haiku" 271 }, 272 "data_collection_described": { 273 "applies": true, 274 "answer": true, 275 "justification": "LeetCodeJava collection described: 50 medium + 50 hard problems randomly sampled from LeetCode, solutions sourced from GitHub repository doocs/leetcode (cited as ref [21]).", 276 "source": "haiku" 277 }, 278 "recruitment_methods_described": { 279 "applies": false, 280 "answer": false, 281 "justification": "No human participants — standard benchmark datasets used without human recruitment.", 282 "source": "haiku" 283 }, 284 "data_pipeline_documented": { 285 "applies": true, 286 "answer": true, 287 "justification": "Evaluation pipeline documented: JaCoCo for coverage reporting (with uncovered lines/branches fed to Planner), PiTest for mutation generation, compilation/execution validation steps described.", 288 "source": "haiku" 289 } 290 }, 291 "contamination": { 292 "training_cutoff_stated": { 293 "applies": true, 294 "answer": false, 295 "justification": "No training data cutoff dates are stated for Llama 3.1 70B or DeepSeek R1 despite evaluating on HumanEvalJava, a publicly available benchmark.", 296 "source": "haiku" 297 }, 298 "train_test_overlap_discussed": { 299 "applies": true, 300 "answer": true, 301 "justification": "Section 6.2 explicitly acknowledges data leakage risk and proposes mutation score as mitigation since mutated programs are unlikely to appear in pretraining corpora.", 302 "source": "haiku" 303 }, 304 "benchmark_contamination_addressed": { 305 "applies": true, 306 "answer": true, 307 "justification": "HumanEvalJava contamination is acknowledged and the mutation score metric is proposed as a more reliable evaluation because mutants are unique at test time.", 308 "source": "haiku" 309 } 310 }, 311 "human_studies": { 312 "pre_registered": { 313 "applies": false, 314 "answer": false, 315 "justification": "No human participants.", 316 "source": "haiku" 317 }, 318 "irb_or_ethics_approval": { 319 "applies": false, 320 "answer": false, 321 "justification": "No human participants.", 322 "source": "haiku" 323 }, 324 "demographics_reported": { 325 "applies": false, 326 "answer": false, 327 "justification": "No human participants.", 328 "source": "haiku" 329 }, 330 "inclusion_exclusion_criteria": { 331 "applies": false, 332 "answer": false, 333 "justification": "No human participants.", 334 "source": "haiku" 335 }, 336 "randomization_described": { 337 "applies": false, 338 "answer": false, 339 "justification": "No human participants.", 340 "source": "haiku" 341 }, 342 "blinding_described": { 343 "applies": false, 344 "answer": false, 345 "justification": "No human participants.", 346 "source": "haiku" 347 }, 348 "attrition_reported": { 349 "applies": false, 350 "answer": false, 351 "justification": "No human participants.", 352 "source": "haiku" 353 } 354 }, 355 "cost_and_practicality": { 356 "inference_cost_reported": { 357 "applies": true, 358 "answer": false, 359 "justification": "No systematic inference time or cost figures reported; only qualitative remarks that DeepSeek sometimes produced 10,000+ token outputs 'taking hours to complete a single test file'.", 360 "source": "haiku" 361 }, 362 "compute_budget_stated": { 363 "applies": true, 364 "answer": false, 365 "justification": "Hardware specs are listed (Precision 7960 Tower, dual RTX 6000 Ada GPUs) but no total experiment runtime or compute budget is stated.", 366 "source": "haiku" 367 } 368 } 369 } 370 }, 371 "claims": [ 372 { 373 "claim": "CANDOR achieves comparable line and branch coverage to EvoSuite across all three datasets", 374 "evidence": "Table 1: differences between CANDOR and EvoSuite in line/branch coverage are ≤0.031 and not statistically significant (p>0.05) on HumanEvalJava and LeetCode-Medium; LeetCode-Hard line difference also not significant", 375 "supported": "strong" 376 }, 377 { 378 "claim": "CANDOR significantly outperforms EvoSuite in mutation score by at least 4.9 percentage points on all datasets", 379 "evidence": "Table 1: mutation scores 0.980 vs 0.858 (HumanEvalJava), 0.939 vs 0.845 (LeetCode-Medium), 0.937 vs 0.888 (LeetCode-Hard); all differences statistically significant (p<1e-4)", 380 "supported": "strong" 381 }, 382 { 383 "claim": "CANDOR outperforms fine-tuned SOTA oracle generator TOGLL by at least 21.1 percentage points", 384 "evidence": "Figure 4: EVO-CANDOR vs TOGLL gaps range 0.255–0.211 on correct code and 0.254–0.211 on faulty code; A12 effect sizes 0.920 and 0.960 with p<1e-4", 385 "supported": "strong" 386 }, 387 { 388 "claim": "Panel discussion is the most critical component for oracle correctness", 389 "evidence": "Table 2: removing panel discussion reduces oracle correctness by 0.067–0.086 across datasets (all p<1e-2), larger than removing Requirement Engineer (0.007–0.028)", 390 "supported": "strong" 391 }, 392 { 393 "claim": "The Planner agent is critical for test prefix quality", 394 "evidence": "Table 2: removing Planner reduces line coverage by 0.050–0.099, branch coverage by 0.046–0.130, and mutation score by 0.070–0.111; all differences statistically significant (p<1e-4)", 395 "supported": "strong" 396 }, 397 { 398 "claim": "CANDOR is robust to increasing code complexity with only slight performance drops from Medium to Hard LeetCode", 399 "evidence": "Table 1: line coverage drops only 0.001 (0.990→0.989) and mutation score 0.002 (0.939→0.937) from Medium to Hard; authors attribute this to LLM pretraining breadth", 400 "supported": "moderate" 401 } 402 ], 403 "methodology_tags": [ 404 "benchmark-eval", 405 "case-study" 406 ], 407 "key_findings": "CANDOR, a multi-agent LLM framework using 8 specialized agents, achieves comparable line/branch coverage to EvoSuite (the SBST SOTA) while outperforming it in mutation score by at least 4.9pp across all datasets without fine-tuning or external tools. In oracle generation, CANDOR outperforms the fine-tuned SOTA baseline TOGLL by at least 21.1 percentage points using only off-the-shelf LLMs, demonstrating prompt-engineering-based multi-agent approaches can surpass fine-tuned models on this task. Ablation studies confirm the panel discussion mechanism accounts for 6.7–8.6pp of oracle accuracy, and the Planner agent accounts for up to 13pp of coverage; over 70% of oracle evaluations exhibited Panelist disagreements requiring Curator resolution.", 408 "red_flags": [ 409 { 410 "flag": "Code not released", 411 "detail": "Authors state 'we plan to release the code publicly upon paper acceptance' — standard promise that prevents independent reproduction at time of publication." 412 }, 413 { 414 "flag": "No variance reported", 415 "detail": "Despite repeating experiments 3 times, only averages are reported with no standard deviations, confidence intervals, or error bars in any table or figure." 416 }, 417 { 418 "flag": "Table inconsistency", 419 "detail": "Table 1 reports CANDOR HumanEvalJava branch coverage as 0.950, but Table 2 reports it as 0.970 — a 2pp discrepancy with no explanation." 420 }, 421 { 422 "flag": "HumanEvalJava contamination unresolved", 423 "detail": "HumanEvalJava is publicly available and almost certainly in Llama 3.1 and DeepSeek R1 pretraining data; the proposed mitigation (mutation score) is incomplete since test prefixes and oracles may still be memorized." 424 }, 425 { 426 "flag": "Scope severely limited", 427 "detail": "CANDOR only handles Java methods with no dependencies on user-defined or external classes — this excludes the vast majority of real-world production code, which the paper acknowledges but does not quantify." 428 }, 429 { 430 "flag": "No inference cost reported", 431 "detail": "A framework involving 8 LLM agents per test file has significant computational overhead; no systematic latency, throughput, or API cost figures are provided despite practitioners needing this for adoption decisions." 432 }, 433 { 434 "flag": "Undisclosed Huawei affiliation conflict", 435 "detail": "Co-author Kui Liu is from Huawei's Software Engineering Application Technology Lab, which has direct commercial interest in automated testing tools; no conflict-of-interest statement is provided." 436 } 437 ], 438 "cited_papers": [ 439 { 440 "title": "EvoSuite: Automatic Test Suite Generation for Object-Oriented Software (Fraser & Arcuri, 2011)", 441 "relevance": "Primary baseline for test prefix generation; the paper positions CANDOR as achieving comparable coverage to EvoSuite while surpassing it in mutation score." 442 }, 443 { 444 "title": "TOGLL: Correct and Strong Test Oracle Generation with LLMs (Hossain & Dwyer, 2024)", 445 "relevance": "Primary SOTA baseline for oracle generation; CANDOR's main claim is outperforming this fine-tuned approach by ≥21.1pp using only prompt engineering." 446 }, 447 { 448 "title": "Using Large Language Models to Generate JUnit Tests: An Empirical Study (Siddiq et al., 2024)", 449 "relevance": "LLM-Empirical baseline representing prompt-engineering-only end-to-end test generation; most direct predecessor to CANDOR." 450 }, 451 { 452 "title": "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (Guo et al., 2025)", 453 "relevance": "The reasoning LLM used as Panelist agents in CANDOR's panel discussion; selected for strong reasoning capability among open-source models." 454 }, 455 { 456 "title": "TOGA: A Neural Method for Test Oracle Generation (Dinella et al., 2022)", 457 "relevance": "First LLM-based (CodeBERT fine-tuned) oracle generator; positioned as foundational prior work motivating the specification-based oracle generation line of research." 458 }, 459 { 460 "title": "ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation (Tang et al., 2024)", 461 "relevance": "Reports EvoSuite remains SOTA for coverage despite LLM advances; motivates CANDOR's multi-agent approach to close this gap." 462 }, 463 { 464 "title": "A Practical Guide for Using Statistical Tests to Assess Randomized Algorithms in Software Engineering (Arcuri & Briand, 2011)", 465 "relevance": "Methodological reference for the Wilcoxon Signed Rank test used throughout the evaluation as per Section 4.3." 466 }, 467 { 468 "title": "Large Language Model Based Multi-Agents: A Survey of Progress and Challenges (Guo et al., 2024)", 469 "relevance": "Background survey on multi-agent LLM systems that provides conceptual grounding for CANDOR's agent orchestration approach." 470 } 471 ], 472 "engagement_factors": { 473 "practical_relevance": { 474 "score": 2, 475 "justification": "Addresses a real developer pain point with a working system showing strong results, but scope limited to dependency-free Java methods and code not yet released reduces immediate adoptability." 476 }, 477 "surprise_contrarian": { 478 "score": 1, 479 "justification": "The finding that prompt-engineering beats fine-tuned TOGLL is somewhat surprising and noteworthy, but the general trend of LLMs matching traditional tools was already established." 480 }, 481 "fear_safety": { 482 "score": 0, 483 "justification": "No safety or risk implications; purely a software engineering productivity tool with no adversarial or alignment concerns." 484 }, 485 "drama_conflict": { 486 "score": 1, 487 "justification": "The 'Hallucination to Consensus' framing and David Hume quote add narrative interest, but there is no real methodological controversy or surprising reversal." 488 }, 489 "demo_ability": { 490 "score": 1, 491 "justification": "Code not yet released; framework is described in enough detail to partially replicate but requires significant open-source LLM infrastructure and Java toolchain setup." 492 }, 493 "brand_recognition": { 494 "score": 0, 495 "justification": "University of Limerick, University of Ottawa, and Huawei are not prominent AI research brands; Lero Centre is specialized but not widely known in the broader ML community." 496 } 497 }, 498 "hn_data": { 499 "threads": [ 500 { 501 "hn_id": "40584327", 502 "title": "To Believe or Not Believe Your LLM", 503 "points": 58, 504 "comments": 17, 505 "url": "https://news.ycombinator.com/item?id=40584327" 506 }, 507 { 508 "hn_id": "23512220", 509 "title": "Ear2Face", 510 "points": 3, 511 "comments": 0, 512 "url": "https://news.ycombinator.com/item?id=23512220" 513 }, 514 { 515 "hn_id": "40641266", 516 "title": "To Believe or Not to Believe Your LLM", 517 "points": 2, 518 "comments": 0, 519 "url": "https://news.ycombinator.com/item?id=40641266" 520 }, 521 { 522 "hn_id": "38617050", 523 "title": "A Neural Corpus Indexer for Document Retrieval", 524 "points": 2, 525 "comments": 0, 526 "url": "https://news.ycombinator.com/item?id=38617050" 527 }, 528 { 529 "hn_id": "27441912", 530 "title": "Cluster Monte Carlo: Modeling Dense Star Clusters in the Milky Way and Beyond", 531 "points": 2, 532 "comments": 0, 533 "url": "https://news.ycombinator.com/item?id=27441912" 534 }, 535 { 536 "hn_id": "23544629", 537 "title": "Vulnerability Analysis of 2500 Docker Hub Images", 538 "points": 2, 539 "comments": 0, 540 "url": "https://news.ycombinator.com/item?id=23544629" 541 }, 542 { 543 "hn_id": "23426018", 544 "title": "Generate a face image of a subject given an ear image as the input [pdf]", 545 "points": 1, 546 "comments": 1, 547 "url": "https://news.ycombinator.com/item?id=23426018" 548 }, 549 { 550 "hn_id": "44022405", 551 "title": "Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models", 552 "points": 1, 553 "comments": 0, 554 "url": "https://news.ycombinator.com/item?id=44022405" 555 } 556 ], 557 "top_points": 58, 558 "total_points": 71, 559 "total_comments": 18 560 } 561 }