{"id":"W1811792547","doi":"10.24908/pceea.v0i0.5826","title":"SHEDDING LIGHT ON CUSTOMER REQUIREMENT SPECIFICATIONS, FUNCTIONAL SPECIFICATIONS AND REQUIREMENTS LISTS – HOW ENGINEERS LEARN THE CORRECT DOCUMENTATION OF REQUIREMENTS","year":2015,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Documentation; Requirement; Requirements engineering; Requirements analysis; Computer science; Product (mathematics); Requirements elicitation; New product development; Process (computing); Requirements management; Functional requirement; Product design specification; Software engineering; Software requirements specification; Non-functional requirement; Process management; Technical documentation; Systems engineering; Product design; Engineering; Software development; Business; Software; Programming language; Software design","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03620037,0.001083888,0.0007229008,0.004751099,0.002434495,0.01451623,0.002251028,0.004397845,0.003883355],"category_scores_gemma":[0.08470359,0.001320515,0.0006499583,0.002888583,0.009931273,0.03112346,0.004568182,0.006373386,0.002599692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004949545,"about_ca_system_score_gemma":0.00902836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009429482,"about_ca_topic_score_gemma":0.009093532,"domain_scores_codex":[0.9505668,0.03098919,0.002037683,0.002179897,0.0127933,0.00143313],"domain_scores_gemma":[0.9120022,0.05759608,0.004189175,0.009639665,0.01532437,0.00124847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001251705,0.0003980084,0.003897287,0.001329174,0.00004329102,0.0003178754,0.05566787,0.003178662,0.006743447,0.2213615,0.02738195,0.6795557],"study_design_scores_gemma":[0.00009051409,0.0005262953,0.006240719,0.004726264,0.00006975974,0.001479986,0.05437084,0.01197464,0.0159664,0.2150934,0.6890838,0.0003774058],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1123652,0.0244268,0.630863,0.1024349,0.001506505,0.0003823936,0.0002842682,0.002581117,0.1251558],"genre_scores_gemma":[0.4934082,0.0244041,0.4188584,0.0138397,0.000509833,0.0003738772,0.000900926,0.002326074,0.04537888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03620037,"threshold_uncertainty_score":0.1914481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04709178723586475,"score_gpt":0.2622758525711975,"score_spread":0.2151840653353327,"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."}}