From df2c7409c0ecd1a07ed1df096a136b5b1ecfbac9 Mon Sep 17 00:00:00 2001 From: "will.anderson" Date: Thu, 11 Jun 2026 11:58:43 -0500 Subject: [PATCH] =?UTF-8?q?feat(steward):=20behavioral=20profiling=20and?= =?UTF-8?q?=20continuity=20detection=20=E2=80=94=20drift,=20discontinuity,?= =?UTF-8?q?=20identity=20anomaly?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- stewardship.el | 277 ++++++++++++++++++++++++++++++ stewardship.elh | 5 + tests/test_stewardship_profile.el | 153 +++++++++++++++++ 3 files changed, 435 insertions(+) create mode 100644 tests/test_stewardship_profile.el diff --git a/stewardship.el b/stewardship.el index 6d96a00..437535b 100644 --- a/stewardship.el +++ b/stewardship.el @@ -138,3 +138,280 @@ fn steward_cgi_check(action: String) -> String { return "{\"approved\":true}" } + +// steward_fingerprint_session — extract a 6-dimension behavioral fingerprint from the current input. +// Stores a BehaviorSample node in engram and returns the fingerprint as JSON. +// Dimensions: avg_word_len, punct, len, question, formality, time +fn steward_fingerprint_session(input: String, session_id: String) -> String { + let input_len: Int = str_len(input) + + // Dimension 1: avg_word_len bucket + // Count space-separated words and total char length to approximate avg word length. + // We count spaces to approximate word count (words ≈ spaces + 1), then divide. + // Bucket: short (1-4 avg) = 1, medium (4-6) = 2, long (6+) = 3 + // Use char counts: each space increments word_count proxy. + // We iterate through the string checking for spaces using str_slice + str_eq. + // To avoid a loop (EL has while), we approximate by checking every 5th char. + // Simpler approach: count non-space chars / (spaces+1). + // We use a while loop with a counter index. + let wl_spaces: Int = 0 + let wl_i: Int = 0 + while wl_i < input_len { + let ch: String = str_slice(input, wl_i, wl_i + 1) + let wl_spaces = if str_eq(ch, " ") { wl_spaces + 1 } else { wl_spaces } + let wl_i = wl_i + 1 + } + let wl_word_count: Int = wl_spaces + 1 + // non-space chars ≈ total len minus spaces + let wl_char_count: Int = input_len - wl_spaces + // avg word len = char_count / word_count (integer division) + let wl_avg: Int = if wl_word_count > 0 { wl_char_count / wl_word_count } else { 0 } + let avg_word_len: Int = if wl_avg <= 4 { 1 } else { if wl_avg <= 6 { 2 } else { 3 } } + + // Dimension 2: punctuation_style + // Count "." "?" "!" "," in input + let ps_i: Int = 0 + let ps_count: Int = 0 + while ps_i < input_len { + let ch: String = str_slice(input, ps_i, ps_i + 1) + let is_punct: Bool = str_eq(ch, ".") || str_eq(ch, "?") || str_eq(ch, "!") || str_eq(ch, ",") + let ps_count = if is_punct { ps_count + 1 } else { ps_count } + let ps_i = ps_i + 1 + } + let punctuation_style: Int = if ps_count > 3 { 2 } else { 1 } + + // Dimension 3: message_len_bucket + let message_len_bucket: Int = if input_len < 50 { 1 } else { if input_len <= 200 { 2 } else { 3 } } + + // Dimension 4: question_ratio — does input contain "?" + let question_ratio: Int = if str_contains(input, "?") { 1 } else { 0 } + + // Dimension 5: formality_signal + let is_formal: Bool = str_contains(input, "please") + || str_contains(input, "could you") + || str_contains(input, "would you") + || str_contains(input, "I would") + let formality_signal: Int = if is_formal { 2 } else { 1 } + + // Dimension 6: time_bucket from time_now() + // time_now() returns unix ms. Extract hour-of-day (UTC). + // hours_since_epoch = ms / 3600000; hour_of_day = hours_since_epoch % 24 + // Avoid % bug: use x - ((x/24)*24) with repeated addition for *24. + let tb_ms: Int = time_now() + let tb_hours: Int = tb_ms / 3600000 + let tb_q: Int = tb_hours / 24 + // tb_q * 24 via repeated addition + let tb_q24: Int = tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + tb_q + let tb_hour: Int = tb_hours - tb_q24 + let time_bucket: Int = if tb_hour < 6 { 1 } else { if tb_hour < 12 { 2 } else { if tb_hour < 18 { 3 } else { 4 } } } + + // Store BehaviorSample node in engram + let wl_str: String = int_to_str(avg_word_len) + let ps_str: String = int_to_str(punctuation_style) + let lb_str: String = int_to_str(message_len_bucket) + let qr_str: String = int_to_str(question_ratio) + let fs_str: String = int_to_str(formality_signal) + let tb_str: String = int_to_str(time_bucket) + + let sample_content: String = "BEHAVIOR_SAMPLE session=" + session_id + + " avg_word_len=" + wl_str + + " punct=" + ps_str + + " len=" + lb_str + + " question=" + qr_str + + " formality=" + fs_str + + " time=" + tb_str + let sample_tags: String = "[\"behavior\",\"BehaviorSample\",\"stewardship\"]" + let discard: String = engram_node_full( + sample_content, + "BehaviorSample", + "behavior:" + session_id, + el_from_float(0.6), + el_from_float(0.5), + el_from_float(0.8), + "Episodic", + sample_tags + ) + + return "{\"avg_word_len\":\"" + wl_str + "\",\"punct\":\"" + ps_str + "\",\"len\":\"" + lb_str + "\",\"question\":\"" + qr_str + "\",\"formality\":\"" + fs_str + "\",\"time\":\"" + tb_str + "\"}" +} + +// extract_dim — helper to parse a dimension value from a BEHAVIOR_SAMPLE content string. +// Finds "key=" in content and returns the single character after it, or "0" if not found. +fn extract_dim(content: String, key: String) -> String { + let key_len: Int = str_len(key) + let pos: Int = str_index_of(content, key) + if pos < 0 { return "0" } + let val_start: Int = pos + key_len + let val: String = str_slice(content, val_start, val_start + 1) + if str_eq(val, "") { return "0" } + return val +} + +// steward_build_baseline — load last 20 BehaviorSample nodes and compute mode for each dimension. +// Returns {"baseline":{...},"sample_count":""} or {"baseline":null,"sample_count":""} if < 5 samples. +fn steward_build_baseline() -> String { + let results: String = engram_search_json("BEHAVIOR_SAMPLE", 20) + let no_results: Bool = str_eq(results, "") || str_eq(results, "[]") + if no_results { + return "{\"baseline\":null,\"sample_count\":\"0\"}" + } + + let total: Int = json_array_len(results) + if total < 5 { + return "{\"baseline\":null,\"sample_count\":\"" + int_to_str(total) + "\"}" + } + + // Tally counts for each dimension value (1,2,3,4) across all samples. + // avg_word_len: values 1-3 + let wl1: Int = 0 + let wl2: Int = 0 + let wl3: Int = 0 + // punct: values 1-2 + let ps1: Int = 0 + let ps2: Int = 0 + // len: values 1-3 + let lb1: Int = 0 + let lb2: Int = 0 + let lb3: Int = 0 + // question: values 0-1 + let qr0: Int = 0 + let qr1: Int = 0 + // formality: values 1-2 + let fs1: Int = 0 + let fs2: Int = 0 + // time: values 1-4 + let tb1: Int = 0 + let tb2: Int = 0 + let tb3: Int = 0 + let tb4: Int = 0 + + let bi: Int = 0 + while bi < total { + let node: String = json_array_get(results, bi) + let content: String = json_get(node, "content") + + let wl: String = extract_dim(content, "avg_word_len=") + let wl1 = if str_eq(wl, "1") { wl1 + 1 } else { wl1 } + let wl2 = if str_eq(wl, "2") { wl2 + 1 } else { wl2 } + let wl3 = if str_eq(wl, "3") { wl3 + 1 } else { wl3 } + + let ps: String = extract_dim(content, "punct=") + let ps1 = if str_eq(ps, "1") { ps1 + 1 } else { ps1 } + let ps2 = if str_eq(ps, "2") { ps2 + 1 } else { ps2 } + + let lb: String = extract_dim(content, "len=") + let lb1 = if str_eq(lb, "1") { lb1 + 1 } else { lb1 } + let lb2 = if str_eq(lb, "2") { lb2 + 1 } else { lb2 } + let lb3 = if str_eq(lb, "3") { lb3 + 1 } else { lb3 } + + let qr: String = extract_dim(content, "question=") + let qr0 = if str_eq(qr, "0") { qr0 + 1 } else { qr0 } + let qr1 = if str_eq(qr, "1") { qr1 + 1 } else { qr1 } + + let fs: String = extract_dim(content, "formality=") + let fs1 = if str_eq(fs, "1") { fs1 + 1 } else { fs1 } + let fs2 = if str_eq(fs, "2") { fs2 + 1 } else { fs2 } + + let tb: String = extract_dim(content, "time=") + let tb1 = if str_eq(tb, "1") { tb1 + 1 } else { tb1 } + let tb2 = if str_eq(tb, "2") { tb2 + 1 } else { tb2 } + let tb3 = if str_eq(tb, "3") { tb3 + 1 } else { tb3 } + let tb4 = if str_eq(tb, "4") { tb4 + 1 } else { tb4 } + + let bi = bi + 1 + } + + // Mode for avg_word_len (1, 2, or 3) + let mode_wl: String = if wl1 >= wl2 && wl1 >= wl3 { "1" } else { if wl2 >= wl3 { "2" } else { "3" } } + + // Mode for punct (1 or 2) + let mode_ps: String = if ps1 >= ps2 { "1" } else { "2" } + + // Mode for len (1, 2, or 3) + let mode_lb: String = if lb1 >= lb2 && lb1 >= lb3 { "1" } else { if lb2 >= lb3 { "2" } else { "3" } } + + // Mode for question (0 or 1) + let mode_qr: String = if qr0 >= qr1 { "0" } else { "1" } + + // Mode for formality (1 or 2) + let mode_fs: String = if fs1 >= fs2 { "1" } else { "2" } + + // Mode for time (1, 2, 3, or 4) + let mode_tb_12: String = if tb1 >= tb2 { "1" } else { "2" } + let mode_tb_34: String = if tb3 >= tb4 { "3" } else { "4" } + let mode_tb_best12: Int = if str_eq(mode_tb_12, "1") { tb1 } else { tb2 } + let mode_tb_best34: Int = if str_eq(mode_tb_34, "3") { tb3 } else { tb4 } + let mode_tb: String = if mode_tb_best12 >= mode_tb_best34 { mode_tb_12 } else { mode_tb_34 } + + let baseline_json: String = "{\"avg_word_len\":\"" + mode_wl + "\",\"punct\":\"" + mode_ps + "\",\"len\":\"" + mode_lb + "\",\"question\":\"" + mode_qr + "\",\"formality\":\"" + mode_fs + "\",\"time\":\"" + mode_tb + "\"}" + + return "{\"baseline\":" + baseline_json + ",\"sample_count\":\"" + int_to_str(total) + "\"}" +} + +// steward_check_continuity — compare the current fingerprint against the established baseline. +// Returns a JSON result with status, score, action, and optional message. +fn steward_check_continuity(current_fingerprint: String, session_id: String) -> String { + let baseline_result: String = steward_build_baseline() + let baseline_val: String = json_get(baseline_result, "baseline") + + // If baseline is null (< 5 samples), return learning status + let is_null: Bool = str_eq(baseline_val, "") || str_eq(baseline_val, "null") + if is_null { + return "{\"status\":\"learning\",\"message\":\"building baseline\",\"action\":\"pass\"}" + } + + // Extract current fingerprint dimensions + let cur_wl: String = json_get(current_fingerprint, "avg_word_len") + let cur_ps: String = json_get(current_fingerprint, "punct") + let cur_lb: String = json_get(current_fingerprint, "len") + let cur_qr: String = json_get(current_fingerprint, "question") + let cur_fs: String = json_get(current_fingerprint, "formality") + let cur_tb: String = json_get(current_fingerprint, "time") + + // Extract baseline dimensions + let base_wl: String = json_get(baseline_val, "avg_word_len") + let base_ps: String = json_get(baseline_val, "punct") + let base_lb: String = json_get(baseline_val, "len") + let base_qr: String = json_get(baseline_val, "question") + let base_fs: String = json_get(baseline_val, "formality") + let base_tb: String = json_get(baseline_val, "time") + + // Count mismatches + let m_wl: Int = if str_eq(cur_wl, base_wl) { 0 } else { 1 } + let m_ps: Int = if str_eq(cur_ps, base_ps) { 0 } else { 1 } + let m_lb: Int = if str_eq(cur_lb, base_lb) { 0 } else { 1 } + let m_qr: Int = if str_eq(cur_qr, base_qr) { 0 } else { 1 } + let m_fs: Int = if str_eq(cur_fs, base_fs) { 0 } else { 1 } + let m_tb: Int = if str_eq(cur_tb, base_tb) { 0 } else { 1 } + let mismatches: Int = m_wl + m_ps + m_lb + m_qr + m_fs + m_tb + let score_str: String = int_to_str(mismatches) + + if mismatches <= 1 { + return "{\"status\":\"consistent\",\"score\":\"" + score_str + "\",\"action\":\"pass\"}" + } + + if mismatches <= 3 { + let detail: String = "session=" + session_id + " mismatches=" + score_str + steward_log_event("behavior_drift", detail) + return "{\"status\":\"drift\",\"score\":\"" + score_str + "\",\"action\":\"annotate\",\"message\":\"behavioral drift detected \\u2014 responding with attentiveness\"}" + } + + if mismatches <= 5 { + let detail: String = "session=" + session_id + " mismatches=" + score_str + steward_log_event("continuity_concern", detail) + return "{\"status\":\"discontinuity\",\"score\":\"" + score_str + "\",\"action\":\"soft_check\",\"message\":\"significant pattern change \\u2014 gentle continuity check appropriate\"}" + } + + // All 6 mismatched — anomaly + let detail: String = "session=" + session_id + " mismatches=6" + steward_log_event("identity_anomaly", detail) + return "{\"status\":\"anomaly\",\"score\":\"6\",\"action\":\"identity_check\",\"message\":\"behavioral pattern strongly inconsistent with established profile\"}" +} + +// steward_session_check — convenience wrapper: fingerprint + continuity check in one call. +// Called from the composition layer each turn. +fn steward_session_check(input: String, session_id: String) -> String { + let fingerprint: String = steward_fingerprint_session(input, session_id) + let result: String = steward_check_continuity(fingerprint, session_id) + return result +} diff --git a/stewardship.elh b/stewardship.elh index d5d0fda..4198268 100644 --- a/stewardship.elh +++ b/stewardship.elh @@ -8,3 +8,8 @@ extern fn steward_cgi_check(action: String) -> String // External callers have no business invoking this directly — use steward_align, // steward_validate_imprint, or steward_cgi_check, which call it at the correct points. extern fn steward_log_event(kind: String, detail: String) -> Void +// Behavioral profiling and continuity detection (Layer 2 — session fingerprinting). +extern fn steward_fingerprint_session(input: String, session_id: String) -> String +extern fn steward_build_baseline() -> String +extern fn steward_check_continuity(current_fingerprint: String, session_id: String) -> String +extern fn steward_session_check(input: String, session_id: String) -> String diff --git a/tests/test_stewardship_profile.el b/tests/test_stewardship_profile.el new file mode 100644 index 0000000..74baa7d --- /dev/null +++ b/tests/test_stewardship_profile.el @@ -0,0 +1,153 @@ +// test_stewardship_profile.el — tests for behavioral profiling and continuity detection +// Layer 2 (Stewardship): steward_fingerprint_session, steward_build_baseline, +// steward_check_continuity, steward_session_check + +import "stewardship.el" + +// test_fingerprint_short_casual — short casual input returns JSON with all 6 fields present. +// Input: "hey whats up" (12 chars, no punctuation, no formal markers, no question) +fn test_fingerprint_short_casual() -> Bool { + let result: String = steward_fingerprint_session("hey whats up", "test-session-1") + let has_wl: Bool = str_contains(result, "\"avg_word_len\":") + let has_ps: Bool = str_contains(result, "\"punct\":") + let has_lb: Bool = str_contains(result, "\"len\":") + let has_qr: Bool = str_contains(result, "\"question\":") + let has_fs: Bool = str_contains(result, "\"formality\":") + let has_tb: Bool = str_contains(result, "\"time\":") + let all_fields: Bool = has_wl && has_ps && has_lb && has_qr && has_fs && has_tb + println("[test_fingerprint_short_casual] result=" + result + " pass=" + if all_fields { "true" } else { "false" }) + return all_fields +} + +// test_fingerprint_formal_long — long formal input yields formality=2, len=3. +// Input: a formal request over 200 chars with "please" and "could you". +fn test_fingerprint_formal_long() -> Bool { + let long_formal: String = "I would appreciate it if you could you please provide a comprehensive analysis of the behavioral profiling system, including all edge cases and expected outcomes for each possible dimension value that may be encountered." + let result: String = steward_fingerprint_session(long_formal, "test-session-2") + let formality_ok: Bool = str_contains(result, "\"formality\":\"2\"") + let len_ok: Bool = str_contains(result, "\"len\":\"3\"") + println("[test_fingerprint_formal_long] result=" + result + " formality_ok=" + if formality_ok { "true" } else { "false" } + " len_ok=" + if len_ok { "true" } else { "false" }) + return formality_ok && len_ok +} + +// test_fingerprint_question — input containing "?" yields question=1. +fn test_fingerprint_question() -> Bool { + let result: String = steward_fingerprint_session("Could you help me with this?", "test-session-3") + let question_ok: Bool = str_contains(result, "\"question\":\"1\"") + println("[test_fingerprint_question] result=" + result + " pass=" + if question_ok { "true" } else { "false" }) + return question_ok +} + +// test_fingerprint_time_valid — time_bucket field is between 1 and 4 (inclusive). +fn test_fingerprint_time_valid() -> Bool { + let result: String = steward_fingerprint_session("any input at all", "test-session-4") + let t1: Bool = str_contains(result, "\"time\":\"1\"") + let t2: Bool = str_contains(result, "\"time\":\"2\"") + let t3: Bool = str_contains(result, "\"time\":\"3\"") + let t4: Bool = str_contains(result, "\"time\":\"4\"") + let time_valid: Bool = t1 || t2 || t3 || t4 + println("[test_fingerprint_time_valid] result=" + result + " pass=" + if time_valid { "true" } else { "false" }) + return time_valid +} + +// test_baseline_no_data — with a fresh/empty engram, sample_count is "0" and baseline is null. +// Note: in a real test environment there may be pre-existing nodes; this test verifies +// the response shape is always valid JSON with "sample_count" and "baseline" keys. +fn test_baseline_no_data() -> Bool { + let result: String = steward_build_baseline() + let has_baseline_key: Bool = str_contains(result, "\"baseline\":") + let has_sample_count: Bool = str_contains(result, "\"sample_count\":") + let is_null_or_obj: Bool = str_contains(result, "\"baseline\":null") || str_contains(result, "\"baseline\":{") + let valid: Bool = has_baseline_key && has_sample_count && is_null_or_obj + println("[test_baseline_no_data] result=" + result + " pass=" + if valid { "true" } else { "false" }) + return valid +} + +// test_check_continuity_learning — when baseline returns null (< 5 samples), status == "learning". +// We simulate by calling steward_check_continuity with a fingerprint and checking the response +// when there are not enough samples stored yet. +fn test_check_continuity_learning() -> Bool { + // Provide a fingerprint JSON string as if returned by steward_fingerprint_session. + let fake_fp: String = "{\"avg_word_len\":\"1\",\"punct\":\"1\",\"len\":\"1\",\"question\":\"0\",\"formality\":\"1\",\"time\":\"2\"}" + let result: String = steward_check_continuity(fake_fp, "test-session-6") + // If there are < 5 samples in engram, status should be "learning". + // If there happen to be >= 5 samples (pre-existing data), we accept any valid status. + let is_learning: Bool = str_contains(result, "\"status\":\"learning\"") + let is_other: Bool = str_contains(result, "\"status\":\"consistent\"") + || str_contains(result, "\"status\":\"drift\"") + || str_contains(result, "\"status\":\"discontinuity\"") + || str_contains(result, "\"status\":\"anomaly\"") + let has_status: Bool = is_learning || is_other + println("[test_check_continuity_learning] result=" + result + " has_status=" + if has_status { "true" } else { "false" }) + return has_status +} + +// test_session_check_valid_json — steward_session_check returns valid JSON with "status" field. +fn test_session_check_valid_json() -> Bool { + let result: String = steward_session_check("hello world", "test-session-7") + let has_status: Bool = str_contains(result, "\"status\":") + let has_action: Bool = str_contains(result, "\"action\":") + let valid: Bool = has_status && has_action + println("[test_session_check_valid_json] result=" + result + " pass=" + if valid { "true" } else { "false" }) + return valid +} + +// test_check_continuity_consistent — when current fingerprint matches baseline, status == "consistent". +// We seed engram with several identical BehaviorSample nodes then check against the same fingerprint. +fn test_check_continuity_consistent() -> Bool { + // Seed 6 identical BehaviorSample nodes to establish a baseline + let sample: String = "BEHAVIOR_SAMPLE session=seed avg_word_len=2 punct=1 len=2 question=0 formality=1 time=2" + let tags: String = "[\"behavior\",\"BehaviorSample\",\"stewardship\"]" + let d1: String = engram_node_full(sample, "BehaviorSample", "behavior:seed", el_from_float(0.6), el_from_float(0.5), el_from_float(0.8), "Episodic", tags) + let d2: String = engram_node_full(sample, "BehaviorSample", "behavior:seed", el_from_float(0.6), el_from_float(0.5), el_from_float(0.8), "Episodic", tags) + let d3: String = engram_node_full(sample, "BehaviorSample", "behavior:seed", el_from_float(0.6), el_from_float(0.5), el_from_float(0.8), "Episodic", tags) + let d4: String = engram_node_full(sample, "BehaviorSample", "behavior:seed", el_from_float(0.6), el_from_float(0.5), el_from_float(0.8), "Episodic", tags) + let d5: String = engram_node_full(sample, "BehaviorSample", "behavior:seed", el_from_float(0.6), el_from_float(0.5), el_from_float(0.8), "Episodic", tags) + let d6: String = engram_node_full(sample, "BehaviorSample", "behavior:seed", el_from_float(0.6), el_from_float(0.5), el_from_float(0.8), "Episodic", tags) + // Fingerprint matching the seeded baseline + let fp: String = "{\"avg_word_len\":\"2\",\"punct\":\"1\",\"len\":\"2\",\"question\":\"0\",\"formality\":\"1\",\"time\":\"2\"}" + let result: String = steward_check_continuity(fp, "test-session-8") + let is_consistent: Bool = str_contains(result, "\"status\":\"consistent\"") + println("[test_check_continuity_consistent] result=" + result + " pass=" + if is_consistent { "true" } else { "false" }) + return is_consistent +} + +// test_fingerprint_all_fields_present — verify all 6 keys appear in every fingerprint output. +fn test_fingerprint_all_fields_present() -> Bool { + let result: String = steward_fingerprint_session("Please could you help me understand this complex topic in detail, providing examples and step-by-step explanations that cover all the edge cases I might encounter while working with this system?", "test-session-9") + let has_wl: Bool = str_contains(result, "\"avg_word_len\":") + let has_ps: Bool = str_contains(result, "\"punct\":") + let has_lb: Bool = str_contains(result, "\"len\":") + let has_qr: Bool = str_contains(result, "\"question\":") + let has_fs: Bool = str_contains(result, "\"formality\":") + let has_tb: Bool = str_contains(result, "\"time\":") + let all_present: Bool = has_wl && has_ps && has_lb && has_qr && has_fs && has_tb + println("[test_fingerprint_all_fields_present] result=" + result + " pass=" + if all_present { "true" } else { "false" }) + return all_present +} + +// run_all_tests — execute all test cases and report results. +fn run_all_tests() -> Void { + let r1: Bool = test_fingerprint_short_casual() + let r2: Bool = test_fingerprint_formal_long() + let r3: Bool = test_fingerprint_question() + let r4: Bool = test_fingerprint_time_valid() + let r5: Bool = test_baseline_no_data() + let r6: Bool = test_check_continuity_learning() + let r7: Bool = test_session_check_valid_json() + let r8: Bool = test_check_continuity_consistent() + let r9: Bool = test_fingerprint_all_fields_present() + + let passed: Int = 0 + let passed = if r1 { passed + 1 } else { passed } + let passed = if r2 { passed + 1 } else { passed } + let passed = if r3 { passed + 1 } else { passed } + let passed = if r4 { passed + 1 } else { passed } + let passed = if r5 { passed + 1 } else { passed } + let passed = if r6 { passed + 1 } else { passed } + let passed = if r7 { passed + 1 } else { passed } + let passed = if r8 { passed + 1 } else { passed } + let passed = if r9 { passed + 1 } else { passed } + + println("[test_stewardship_profile] " + int_to_str(passed) + "/9 passed") +}