{"id":"W2898370077","doi":"10.1145/3278122.3278127","title":"Exploring feature interactions without specifications: a controlled experiment","year":2018,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Feature (linguistics); Graph; Control flow; Data mining; Control flow graph; Information flow; Machine learning; Artificial intelligence; Theoretical computer science; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006336342,0.001864579,0.001062835,0.0006237268,0.0008673121,0.001145001,0.002030787,0.00213553,0.00910437],"category_scores_gemma":[0.02226287,0.0009372275,0.0007377997,0.0003818825,0.001317536,0.001915374,0.001453421,0.001563616,0.001478869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003466618,"about_ca_system_score_gemma":0.0008217056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000555728,"about_ca_topic_score_gemma":0.0007118864,"domain_scores_codex":[0.996362,0.0016832,0.0003137146,0.0009077261,0.0004127391,0.000320573],"domain_scores_gemma":[0.9361268,0.05396649,0.002873658,0.003175439,0.00215154,0.001705989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.09270469,0.262244,0.03515805,0.009141103,0.0008879104,0.002919327,0.03121843,0.0120597,0.3456883,0.003115261,0.01109467,0.1937686],"study_design_scores_gemma":[0.06448381,0.5468582,0.1045276,0.0009140912,0.002241747,0.002188339,0.009802209,0.05494941,0.1656755,0.0126045,0.03487502,0.0008796424],"study_design_candidate":"randomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982029,0.0001645888,0.01112289,0.0001276204,0.0001114741,0.004428819,0.0005247779,0.0003968166,0.001094074],"genre_scores_gemma":[0.9241345,0.0002636073,0.05415518,0.0007139015,0.0001719146,0.01491416,0.001073243,0.0002119767,0.004361473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00910437,"threshold_uncertainty_score":0.03351021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.172135168540127,"score_gpt":0.3379760282863929,"score_spread":0.1658408597462659,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}