1
2
3
4 package net.sourceforge.pmd.lang.rule.stat;
5
6 import static net.sourceforge.pmd.lang.rule.stat.StatisticalRule.MINIMUM_DESCRIPTOR;
7 import static net.sourceforge.pmd.lang.rule.stat.StatisticalRule.SIGMA_DESCRIPTOR;
8 import static net.sourceforge.pmd.lang.rule.stat.StatisticalRule.TOP_SCORE_DESCRIPTOR;
9
10 import java.util.Set;
11 import java.util.SortedSet;
12 import java.util.TreeSet;
13
14 import net.sourceforge.pmd.RuleContext;
15 import net.sourceforge.pmd.lang.rule.AbstractRule;
16 import net.sourceforge.pmd.stat.DataPoint;
17 import net.sourceforge.pmd.stat.Metric;
18
19
20
21
22
23
24
25
26 public class StatisticalRuleHelper {
27
28 public static final double DELTA = 0.000005;
29
30 private AbstractRule rule;
31
32 private SortedSet<DataPoint> dataPoints = new TreeSet<DataPoint>();
33
34 private int count = 0;
35 private double total = 0.0;
36
37 public StatisticalRuleHelper(AbstractRule rule) {
38 this.rule = rule;
39 rule.definePropertyDescriptor(SIGMA_DESCRIPTOR);
40 rule.definePropertyDescriptor(MINIMUM_DESCRIPTOR);
41 rule.definePropertyDescriptor(TOP_SCORE_DESCRIPTOR);
42 }
43
44 public void addDataPoint(DataPoint point) {
45 count++;
46 total += point.getScore();
47 dataPoints.add(point);
48 }
49
50 public void apply(RuleContext ctx) {
51
52 double deviation;
53 double minimum = 0.0;
54
55 if (rule.getProperty(SIGMA_DESCRIPTOR) != null) {
56 deviation = getStdDev();
57 double sigma = rule.getProperty(SIGMA_DESCRIPTOR);
58 minimum = getMean() + (sigma * deviation);
59 }
60
61 if (rule.getProperty(MINIMUM_DESCRIPTOR) != null) {
62 double mMin = rule.getProperty(MINIMUM_DESCRIPTOR);
63 if (mMin > minimum) {
64 minimum = mMin;
65 }
66 }
67
68 SortedSet<DataPoint> newPoints = applyMinimumValue(dataPoints, minimum);
69
70 if (rule.getProperty(TOP_SCORE_DESCRIPTOR) != null) {
71 int topScore = rule.getProperty(TOP_SCORE_DESCRIPTOR);
72 if (newPoints.size() >= topScore) {
73 newPoints = applyTopScore(newPoints, topScore);
74 }
75 }
76
77 makeViolations(ctx, newPoints);
78
79 double low = 0.0d;
80 double high = 0.0d;
81 if (!dataPoints.isEmpty()) {
82 low = dataPoints.first().getScore();
83 high = dataPoints.last().getScore();
84 }
85
86 ctx.getReport().addMetric(new Metric(rule.getName(), count, total, low, high, getMean(), getStdDev()));
87
88 dataPoints.clear();
89 }
90
91 private double getMean() {
92 return total / count;
93 }
94
95 private double getStdDev() {
96 if (dataPoints.size() < 2) {
97 return Double.NaN;
98 }
99
100 double mean = getMean();
101 double deltaSq = 0.0;
102 double scoreMinusMean;
103
104 for (DataPoint point: dataPoints) {
105 scoreMinusMean = point.getScore() - mean;
106 deltaSq += scoreMinusMean * scoreMinusMean;
107 }
108
109 return Math.sqrt(deltaSq / (dataPoints.size() - 1));
110 }
111
112 private SortedSet<DataPoint> applyMinimumValue(SortedSet<DataPoint> pointSet, double minValue) {
113 SortedSet<DataPoint> rc = new TreeSet<DataPoint>();
114 double threshold = minValue - DELTA;
115
116 for (DataPoint point: pointSet) {
117 if (point.getScore() > threshold) {
118 rc.add(point);
119 }
120 }
121 return rc;
122 }
123
124 private SortedSet<DataPoint> applyTopScore(SortedSet<DataPoint> points, int topScore) {
125 SortedSet<DataPoint> s = new TreeSet<DataPoint>();
126 DataPoint[] arr = points.toArray(new DataPoint[]{});
127 for (int i = arr.length - 1; i >= (arr.length - topScore); i--) {
128 s.add(arr[i]);
129 }
130 return s;
131 }
132
133 private void makeViolations(RuleContext ctx, Set<DataPoint> p) {
134 for (DataPoint point: p) {
135 rule.addViolationWithMessage(ctx, point.getNode(), point.getMessage(), ((StatisticalRule)rule).getViolationParameters(point));
136 }
137 }
138 }