pstlab /
PLATINUm
| Total Complexity | 72 |
| Total Lines | 773 |
| Duplicated Lines | 15.39 % |
| Changes | 2 | ||
| Bugs | 0 | Features | 0 |
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
Complex classes like it.cnr.istc.pst.platinum.ai.deliberative.strategy.SearchStrategy often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | package it.cnr.istc.pst.platinum.ai.deliberative.strategy; |
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| 42 | public abstract class SearchStrategy extends FrameworkObject implements Comparator<SearchSpaceNode> { |
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| 43 | |||
| 44 | @PlanDataBasePlaceholder |
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| 45 | protected PlanDataBase pdb; // reference to plan data-base |
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| 46 | |||
| 47 | protected Queue<SearchSpaceNode> fringe; // the fringe of the search space |
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| 48 | |||
| 49 | protected String label; // strategy label |
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| 50 | |||
| 51 | protected Map<ComponentValue, List<List<ComponentValue>>> pgraph; // planning graph |
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| 52 | protected Map<DomainComponent, Set<DomainComponent>> dgraph; // dependency graph |
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| 53 | protected List<DomainComponent>[] dhierarchy; // domain hierarchy |
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| 54 | |||
| 55 | protected double schedulingCost; // set scheduling cost |
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| 56 | protected double completionCost; // set completion cost |
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| 57 | protected double planningCost; // general planning cost |
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| 58 | protected double expansionCost; // detailed planning cost |
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| 59 | protected double unificationCost; // detailed unification cost |
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| 60 | |||
| 61 | |||
| 62 | // set client connection |
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| 63 | protected static MongoClient client; |
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| 64 | // prepare collection |
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| 65 | protected MongoCollection<Document> collection; |
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| 66 | |||
| 67 | /** |
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| 68 | * |
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| 69 | * @param label |
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| 70 | */ |
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| 71 | protected SearchStrategy(String label) { |
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| 72 | super(); |
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| 73 | |||
| 74 | // initialize the fringe |
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| 75 | this.fringe = new PriorityQueue<SearchSpaceNode>(this); |
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| 76 | |||
| 77 | // set label |
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| 78 | this.label = label; |
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| 79 | |||
| 80 | // get deliberative property file |
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| 81 | FilePropertyReader properties = new FilePropertyReader( |
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| 82 | FRAMEWORK_HOME + FilePropertyReader.DEFAULT_DELIBERATIVE_PROPERTY); |
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| 83 | // set operation costs from parameters |
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| 84 | this.planningCost = Double.parseDouble(properties.getProperty("expansion-cost")); |
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| 85 | this.expansionCost = Double.parseDouble(properties.getProperty("expansion-cost")); |
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| 86 | this.unificationCost = Double.parseDouble(properties.getProperty("unification-cost")); |
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| 87 | this.schedulingCost = Double.parseDouble(properties.getProperty("scheduling-cost")); |
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| 88 | this.completionCost = Double.parseDouble(properties.getProperty("completion-cost")); |
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| 89 | } |
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| 90 | |||
| 91 | /** |
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| 92 | * |
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| 93 | */ |
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| 94 | @PostConstruct |
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| 95 | protected void init() { |
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| 96 | |||
| 97 | // get domain knowledge |
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| 98 | DomainKnowledge dk = this.pdb.getDomainKnowledge(); |
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| 99 | // get the decomposition tree from the domain theory |
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| 100 | this.pgraph = dk.getDecompositionGraph(); |
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| 101 | // export decomposition graph |
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| 102 | this.exportDecompositionGraph(this.pgraph); |
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| 103 | |||
| 104 | // get dependency graph |
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| 105 | this.dgraph = dk.getDependencyGraph(); |
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| 106 | // export dependency graph |
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| 107 | this.exportDependencyGraph(this.dgraph); |
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| 108 | |||
| 109 | // get domain hierarchy |
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| 110 | this.dhierarchy = dk.getDomainHierarchy(); |
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| 111 | // export hierarchy |
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| 112 | this.exportHierarchyGraph(this.dhierarchy); |
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| 113 | |||
| 114 | |||
| 115 | // get deliberative property file |
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| 116 | FilePropertyReader properties = new FilePropertyReader( |
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| 117 | FRAMEWORK_HOME + FilePropertyReader.DEFAULT_DELIBERATIVE_PROPERTY); |
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| 118 | |||
| 119 | // get mongo |
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| 120 | String mongodb = properties.getProperty("mongodb"); |
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| 121 | // check if exists |
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| 122 | if (mongodb != null && !mongodb.equals("")) { |
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| 123 | |||
| 124 | // create a collection to the DB |
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| 125 | if (client == null) { |
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| 126 | // check DB host |
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| 127 | String dbHost = properties.getProperty("mongodb_host"); |
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| 128 | // create client |
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| 129 | client = MongoClients.create(dbHost); |
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| 130 | } |
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| 131 | |||
| 132 | // get DB |
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| 133 | MongoDatabase db = client.getDatabase(mongodb.trim()); |
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| 134 | // get collection |
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| 135 | this.collection = db.getCollection("planner_search"); |
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| 136 | // remove all data from the collection |
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| 137 | this.collection.drop(); |
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| 138 | } |
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| 139 | } |
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| 140 | |||
| 141 | |||
| 142 | /** |
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| 143 | * Compute the (pessimistic) planning cost of a domain value by analyzing the extracted decomposition graph |
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| 144 | * |
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| 145 | * @param value |
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| 146 | * @return |
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| 147 | */ |
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| 148 | private Map<DomainComponent, Double[]> computeCostProjections(ComponentValue value) { |
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| 149 | |||
| 150 | // set cost |
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| 151 | Map<DomainComponent, Double[]> cost = new HashMap<>(); |
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| 152 | // check if leaf |
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| 153 | if (!this.pgraph.containsKey(value) || |
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| 154 | this.pgraph.get(value).isEmpty()) { |
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| 155 | |||
| 156 | // set cost |
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| 157 | cost.put(value.getComponent(), new Double[] { |
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| 158 | this.unificationCost, |
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| 159 | this.unificationCost |
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| 160 | }); |
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| 161 | |||
| 162 | } else { |
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| 163 | |||
| 164 | // get possible decompositions |
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| 165 | View Code Duplication | for (List<ComponentValue> decomposition : this.pgraph.get(value)) { |
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| 166 | |||
| 167 | // decomposition costs |
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| 168 | Map<DomainComponent, Double[]> dCosts = new HashMap<>(); |
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| 169 | for (ComponentValue subgoal : decomposition) { |
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| 170 | |||
| 171 | // compute planning cost of the subgoal |
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| 172 | Map<DomainComponent, Double[]> update = this.computeCostProjections(subgoal); |
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| 173 | for (DomainComponent c : update.keySet()) { |
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| 174 | |||
| 175 | if (!dCosts.containsKey(c)) { |
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| 176 | // set cost |
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| 177 | dCosts.put(c, new Double[] { |
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| 178 | update.get(c)[0], |
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| 179 | update.get(c)[1] |
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| 180 | }); |
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| 181 | |||
| 182 | } else { |
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| 183 | |||
| 184 | // update cost |
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| 185 | dCosts.put(c, new Double[] { |
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| 186 | dCosts.get(c)[0] + update.get(c)[0], |
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| 187 | dCosts.get(c)[1] + update.get(c)[1] |
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| 188 | }); |
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| 189 | } |
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| 190 | } |
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| 191 | } |
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| 192 | |||
| 193 | // update pessimistic and optimistic projections |
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| 194 | for (DomainComponent c : dCosts.keySet()) { |
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| 195 | if (!cost.containsKey(c)) { |
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| 196 | |||
| 197 | // set cost |
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| 198 | cost.put(c, new Double[] { |
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| 199 | dCosts.get(c)[0], |
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| 200 | dCosts.get(c)[1] |
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| 201 | }); |
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| 202 | |||
| 203 | } else { |
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| 204 | |||
| 205 | // get min and max |
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| 206 | cost.put(c, new Double[] { |
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| 207 | Math.min(cost.get(c)[0], dCosts.get(c)[0]), |
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| 208 | Math.max(cost.get(c)[1], dCosts.get(c)[1]) |
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| 209 | }); |
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| 210 | } |
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| 211 | } |
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| 212 | } |
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| 213 | |||
| 214 | // set cost associated to the value |
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| 215 | if (!cost.containsKey(value.getComponent())) { |
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| 216 | |||
| 217 | // set cost |
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| 218 | cost.put(value.getComponent(), new Double[] { |
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| 219 | this.unificationCost, |
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| 220 | this.unificationCost |
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| 221 | }); |
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| 222 | |||
| 223 | } else { |
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| 224 | |||
| 225 | // weight cost according to the hierarchical value |
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| 226 | cost.put(value.getComponent(), new Double[] { |
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| 227 | this.unificationCost + cost.get(value.getComponent())[0], |
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| 228 | this.unificationCost + cost.get(value.getComponent())[1] |
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| 229 | }); |
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| 230 | } |
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| 231 | } |
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| 232 | |||
| 233 | // get cost |
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| 234 | return cost; |
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| 235 | } |
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| 236 | |||
| 237 | /** |
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| 238 | * Compute the (pessimistic) makespan projection by analyzing the extracted decomposition graph starting |
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| 239 | * from a given value of the domain |
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| 240 | * |
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| 241 | * @param value |
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| 242 | * @return |
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| 243 | */ |
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| 244 | private Map<DomainComponent, Double[]> computeMakespanProjections(ComponentValue value) |
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| 245 | { |
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| 246 | // set data structure |
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| 247 | Map<DomainComponent, Double[]> makespan = new HashMap<>(); |
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| 248 | // check if leaf |
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| 249 | if (!this.pgraph.containsKey(value) || |
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| 250 | this.pgraph.get(value).isEmpty()) { |
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| 251 | |||
| 252 | // set value expected minimum duration |
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| 253 | makespan.put(value.getComponent(), new Double[] { |
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| 254 | (double) value.getDurationLowerBound(), |
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| 255 | (double) value.getDurationUpperBound() |
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| 256 | }); |
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| 257 | |||
| 258 | } else { |
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| 259 | |||
| 260 | // check possible decompositions |
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| 261 | View Code Duplication | for (List<ComponentValue> decomposition : this.pgraph.get(value)) { |
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| 262 | |||
| 263 | // set decomposition makespan |
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| 264 | Map<DomainComponent, Double[]> dMakespan = new HashMap<>(); |
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| 265 | // check subgoals |
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| 266 | for (ComponentValue subgoal : decomposition) { |
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| 267 | |||
| 268 | // recursive call to compute (pessimistic) makespan estimation |
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| 269 | Map<DomainComponent, Double[]> update = this.computeMakespanProjections(subgoal); |
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| 270 | // increment decomposition makespan |
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| 271 | for (DomainComponent c : update.keySet()) { |
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| 272 | |||
| 273 | // check decomposition makespan |
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| 274 | if (!dMakespan.containsKey(c)) { |
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| 275 | // add entry |
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| 276 | dMakespan.put(c, new Double[] { |
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| 277 | update.get(c)[0], |
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| 278 | update.get(c)[1] |
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| 279 | }); |
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| 280 | |||
| 281 | } else { |
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| 282 | |||
| 283 | // increment component's makespan |
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| 284 | dMakespan.put(c, new Double[] { |
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| 285 | dMakespan.get(c)[0] + update.get(c)[0], |
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| 286 | dMakespan.get(c)[1] + update.get(c)[1] |
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| 287 | }); |
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| 288 | } |
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| 289 | } |
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| 290 | } |
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| 291 | |||
| 292 | // update resulting makespan by taking into account the maximum value |
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| 293 | for (DomainComponent c : dMakespan.keySet()) { |
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| 294 | |||
| 295 | // check makespan |
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| 296 | if (!makespan.containsKey(c)) { |
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| 297 | |||
| 298 | // add entry |
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| 299 | makespan.put(c, new Double[] { |
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| 300 | dMakespan.get(c)[0], |
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| 301 | dMakespan.get(c)[1] |
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| 302 | }); |
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| 303 | |||
| 304 | } else { |
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| 305 | |||
| 306 | // set the pessimistic and optimistic projections |
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| 307 | makespan.put(c, new Double[] { |
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| 308 | Math.min(makespan.get(c)[0], dMakespan.get(c)[0]), |
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| 309 | Math.max(makespan.get(c)[1], dMakespan.get(c)[1]) |
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| 310 | }); |
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| 311 | } |
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| 312 | } |
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| 313 | } |
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| 314 | |||
| 315 | // set cost associated to the value |
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| 316 | if (!makespan.containsKey(value.getComponent())) { |
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| 317 | |||
| 318 | // set cost |
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| 319 | makespan.put(value.getComponent(), new Double[] { |
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| 320 | (double) value.getDurationLowerBound(), |
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| 321 | (double) value.getDurationLowerBound() |
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| 322 | }); |
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| 323 | |||
| 324 | } else { |
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| 325 | |||
| 326 | // increment makespan |
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| 327 | makespan.put(value.getComponent(), new Double[] { |
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| 328 | makespan.get(value.getComponent())[0] + ((double) value.getDurationLowerBound()), |
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| 329 | makespan.get(value.getComponent())[1] + ((double) value.getDurationLowerBound()) |
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| 330 | }); |
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| 331 | } |
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| 332 | } |
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| 333 | |||
| 334 | // get the makespan |
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| 335 | return makespan; |
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| 336 | } |
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| 337 | |||
| 338 | |||
| 339 | /** |
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| 340 | * |
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| 341 | * @return |
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| 342 | */ |
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| 343 | public String getLabel() { |
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| 344 | return this.label; |
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| 345 | } |
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| 346 | |||
| 347 | /** |
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| 348 | * |
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| 349 | * @return |
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| 350 | */ |
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| 351 | public int getFringeSize() { |
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| 352 | return this.fringe.size(); |
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| 353 | } |
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| 354 | |||
| 355 | /** |
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| 356 | * |
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| 357 | * @param node |
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| 358 | */ |
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| 359 | public abstract void enqueue(SearchSpaceNode node); |
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| 360 | |||
| 361 | /** |
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| 362 | * |
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| 363 | */ |
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| 364 | @Override |
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| 365 | public abstract int compare(SearchSpaceNode n1, SearchSpaceNode n2); |
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| 366 | |||
| 367 | /** |
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| 368 | * |
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| 369 | * @return |
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| 370 | * @throws EmptyFringeException |
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| 371 | */ |
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| 372 | public SearchSpaceNode dequeue() |
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| 373 | throws EmptyFringeException |
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| 374 | { |
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| 375 | // set next node of the fringe |
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| 376 | SearchSpaceNode next = null; |
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| 377 | try |
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| 378 | { |
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| 379 | // extract the "best" node from the fringe |
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| 380 | next = this.fringe.remove(); |
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| 381 | // store search data record |
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| 382 | this.registerSearchChoice(next); |
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| 383 | } |
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| 384 | catch (NoSuchElementException ex) { |
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| 385 | // empty fringe |
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| 386 | throw new EmptyFringeException("No more nodes in the fringe"); |
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| 387 | } |
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| 388 | |||
| 389 | // get extracted node |
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| 390 | return next; |
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| 391 | } |
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| 392 | |||
| 393 | /** |
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| 394 | * Clear the internal data structures of a search strategy |
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| 395 | */ |
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| 396 | public void clear() { |
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| 397 | // clear queue |
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| 398 | this.fringe.clear(); |
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| 399 | // close DB connection if necessary |
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| 400 | if (client != null) { |
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| 401 | client.close(); |
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| 402 | client = null; |
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| 403 | this.collection = null; |
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| 404 | } |
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| 405 | } |
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| 406 | |||
| 407 | /** |
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| 408 | * |
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| 409 | */ |
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| 410 | public String toString() { |
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| 411 | // JSON like object description |
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| 412 | return "{ \"label\": \"" + this.label + "\" }"; |
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| 413 | } |
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| 414 | |||
| 415 | /** |
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| 416 | * |
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| 417 | * @param node |
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| 418 | */ |
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| 419 | protected void registerSearchChoice(SearchSpaceNode node) |
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| 420 | { |
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| 421 | // check db collection |
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| 422 | if (this.collection != null) { |
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| 423 | // create solving statistic record |
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| 424 | Document doc = new Document("step", node.getId()); |
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| 425 | doc.append("fringe-size", this.fringe.size()); |
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| 426 | doc.append("node-number-of-flaws", node.getNumberOfFlaws()); |
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| 427 | doc.append("node-depth", node.getDepth()); |
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| 428 | |||
| 429 | // consolidated values of metrics |
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| 430 | doc.append("node-plan-cost", node.getPlanCost()); |
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| 431 | doc.append("node-plan-makespan-min", node.getPlanMakespan()[0]); |
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| 432 | doc.append("node-plan-makespan-max", node.getPlanMakespan()[1]); |
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| 433 | |||
| 434 | // heuristic estimation of metrics |
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| 435 | doc.append("node-heuristic-plan-cost-min", node.getPlanHeuristicCost()[0]); |
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| 436 | doc.append("node-heuristic-plan-cost-max", node.getPlanHeuristicCost()[1]); |
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| 437 | doc.append("node-heuristic-plan-makespan-min", node.getPlanHeuristicMakespan()[0]); |
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| 438 | doc.append("node-heuristic-plan-makespan-max", node.getPlanHeuristicMakespan()[1]); |
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| 439 | |||
| 440 | // insert data into the collection |
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| 441 | this.collection.insertOne(doc); |
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| 442 | } |
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| 443 | } |
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| 444 | |||
| 445 | /** |
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| 446 | * This method computes an evaluation concerning the (planning) distance of |
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| 447 | * a given node from a solution plan. |
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| 448 | * |
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| 449 | * Namely the method computes the expected cost the planner should "pay" to refine |
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| 450 | * the given node and obtain a valid solution. The cost takes into account both planning |
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| 451 | * and scheduling decisions. Also, the cost considers possible "gaps" on timelines and |
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| 452 | * tries to estimates the planning effort needed to complete the behaviors of |
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| 453 | * related timelines. |
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| 454 | * |
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| 455 | * The heuristics computes a cost for each component of the domain and |
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| 456 | * takes into account timeline projections and therefore computes a pessimistic |
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| 457 | * and optimistic evaluation. |
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| 458 | * |
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| 459 | * @param node |
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| 460 | * @return |
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| 461 | */ |
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| 462 | protected Map<DomainComponent, Double[]> computeHeuristicCost(SearchSpaceNode node) |
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| 463 | { |
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| 464 | // compute an optimistic and pessimistic estimation of planning operations |
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| 465 | Map<DomainComponent, Double[]> cost = new HashMap<>(); |
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| 466 | // check node flaws and compute heuristic estimation |
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| 467 | for (Flaw flaw : node.getFlaws()) |
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| 468 | { |
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| 469 | // check planning goal |
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| 470 | if (flaw.getType().equals(FlawType.PLAN_REFINEMENT)) |
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| 471 | { |
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| 472 | // get flaw data |
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| 473 | Goal goal = (Goal) flaw; |
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| 474 | // compute cost projections |
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| 475 | Map<DomainComponent, Double[]> update = this.computeCostProjections(goal.getDecision().getValue()); |
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| 476 | // update cost |
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| 477 | for (DomainComponent c : update.keySet()) { |
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| 478 | if (!cost.containsKey(c)) { |
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| 479 | // set cost |
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| 480 | cost.put(c, new Double[] { |
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| 481 | this.planningCost * update.get(c)[0], |
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| 482 | this.planningCost * update.get(c)[1] |
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| 483 | }); |
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| 484 | } |
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| 485 | else { |
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| 486 | // update cost |
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| 487 | cost.put(c, new Double[] { |
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| 488 | cost.get(c)[0] + (this.planningCost * update.get(c)[0]), |
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| 489 | cost.get(c)[1] + (this.planningCost * update.get(c)[1]) |
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| 490 | }); |
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| 491 | } |
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| 492 | } |
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| 493 | } |
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| 494 | |||
| 495 | // check scheduling goal |
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| 496 | View Code Duplication | if (flaw.getType().equals(FlawType.TIMELINE_OVERFLOW)) |
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| 497 | { |
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| 498 | // get component |
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| 499 | DomainComponent comp = flaw.getComponent(); |
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| 500 | // update cost |
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| 501 | if (!cost.containsKey(comp)) { |
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| 502 | // set cost |
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| 503 | cost.put(comp, new Double[] { |
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| 504 | this.schedulingCost * (this.pdb.getDomainKnowledge().getHierarchicalLevelValue(comp) + 1), |
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| 505 | this.schedulingCost * (this.pdb.getDomainKnowledge().getHierarchicalLevelValue(comp) + 1) |
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| 506 | }); |
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| 507 | } |
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| 508 | else { |
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| 509 | // update cost |
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| 510 | cost.put(comp, new Double[] { |
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| 511 | cost.get(comp)[0] + (this.schedulingCost * (this.pdb.getDomainKnowledge().getHierarchicalLevelValue(comp) + 1)), |
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| 512 | cost.get(comp)[1] + (this.schedulingCost * (this.pdb.getDomainKnowledge().getHierarchicalLevelValue(comp) + 1)) |
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| 513 | }); |
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| 514 | } |
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| 515 | } |
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| 516 | |||
| 517 | // check scheduling goal |
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| 518 | View Code Duplication | if (flaw.getType().equals(FlawType.TIMELINE_BEHAVIOR_PLANNING)) |
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| 519 | { |
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| 520 | // get component |
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| 521 | DomainComponent comp = flaw.getComponent(); |
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| 522 | // update cost |
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| 523 | if (!cost.containsKey(comp)) { |
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| 524 | // set cost |
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| 525 | cost.put(comp, new Double[] { |
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| 526 | this.completionCost * (this.pdb.getDomainKnowledge().getHierarchicalLevelValue(comp) + 1), |
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| 527 | this.completionCost * (this.pdb.getDomainKnowledge().getHierarchicalLevelValue(comp) + 1) |
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| 528 | }); |
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| 529 | } |
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| 530 | else { |
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| 531 | // update cost |
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| 532 | cost.put(comp, new Double[] { |
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| 533 | cost.get(comp)[0] + (this.completionCost * (this.pdb.getDomainKnowledge().getHierarchicalLevelValue(comp) + 1)), |
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| 534 | cost.get(comp)[1] + (this.completionCost * (this.pdb.getDomainKnowledge().getHierarchicalLevelValue(comp) + 1)) |
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| 535 | }); |
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| 536 | } |
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| 537 | } |
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| 538 | } |
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| 539 | |||
| 540 | |||
| 541 | // finalize data structure |
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| 542 | for (DomainComponent c : this.pdb.getComponents()) { |
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| 543 | if (!cost.containsKey(c)) { |
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| 544 | cost.put(c, new Double[] { |
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| 545 | (double) 0, |
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| 546 | (double) 0 |
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| 547 | }); |
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| 548 | } |
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| 549 | } |
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| 550 | |||
| 551 | // get cost |
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| 552 | return cost; |
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| 553 | } |
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| 554 | |||
| 555 | /** |
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| 556 | * |
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| 557 | * This method provides an heuristic evaluation of the makespan of domain components. |
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| 558 | * |
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| 559 | * Namely, the method considesrs planning subgoals of a given partial plan and computes |
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| 560 | * a projection of the makespan. The evalution takes into account optmistic and pessimistic |
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| 561 | * projections of timelines |
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| 562 | * |
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| 563 | * @param node |
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| 564 | * @return |
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| 565 | */ |
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| 566 | protected Map<DomainComponent, Double[]> computeHeuristicMakespan(SearchSpaceNode node) |
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| 567 | { |
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| 568 | // initialize makespan projects |
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| 569 | Map<DomainComponent, Double[]> projections = new HashMap<>(); |
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| 570 | // check node flaws and compute heuristic estimation |
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| 571 | for (Flaw flaw : node.getFlaws()) |
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| 572 | { |
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| 573 | // check planning goals |
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| 574 | if (flaw.getType().equals(FlawType.PLAN_REFINEMENT)) |
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| 575 | { |
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| 576 | // get planning goal |
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| 577 | Goal goal = (Goal) flaw; |
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| 578 | // compute optimistic and pessimistic projections of makespan from goals |
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| 579 | Map<DomainComponent, Double[]> update = this.computeMakespanProjections( |
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| 580 | goal.getDecision().getValue()); |
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| 581 | |||
| 582 | // update plan projections |
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| 583 | for (DomainComponent c : update.keySet()) |
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| 584 | { |
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| 585 | // check projection |
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| 586 | if (!projections.containsKey(c)) { |
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| 587 | // set projection |
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| 588 | projections.put(c, |
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| 589 | new Double[] { |
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| 590 | update.get(c)[0], |
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| 591 | update.get(c)[1] |
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| 592 | }); |
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| 593 | } |
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| 594 | else { |
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| 595 | // update projection |
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| 596 | projections.put(c, |
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| 597 | new Double[] { |
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| 598 | projections.get(c)[0] + update.get(c)[0], |
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| 599 | projections.get(c)[1] + update.get(c)[1] |
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| 600 | }); |
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| 601 | } |
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| 602 | } |
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| 603 | } |
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| 604 | } |
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| 605 | |||
| 606 | // finalize data structure |
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| 607 | for (DomainComponent c : this.pdb.getComponents()) { |
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| 608 | if (!projections.containsKey(c)) { |
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| 609 | projections.put(c, new Double[] { |
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| 610 | (double) 0, |
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| 611 | (double) 0 |
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| 612 | }); |
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| 613 | } |
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| 614 | } |
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| 615 | |||
| 616 | // get projections |
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| 617 | return projections; |
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| 618 | } |
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| 619 | |||
| 620 | /** |
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| 621 | * |
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| 622 | * @param graph |
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| 623 | */ |
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| 624 | private void exportHierarchyGraph(List<DomainComponent>[] graph) |
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| 625 | { |
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| 626 | // export graph |
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| 627 | String str = "digraph hierarhcy_graph {\n"; |
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| 628 | str += "\trankdir=TB;\n"; |
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| 629 | str += "\tnode [fontsize=11, style=filled, fillcolor=azure, shape = box]\n"; |
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| 630 | |||
| 631 | |||
| 632 | // check dependencies |
||
| 633 | for (int index = 0; index < graph.length - 1; index++) { |
||
| 634 | // get components at current level |
||
| 635 | List<DomainComponent> currlist = graph[index]; |
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| 636 | // get components at next level |
||
| 637 | List<DomainComponent> nextlist = graph[index + 1]; |
||
| 638 | |||
| 639 | for (DomainComponent curr : currlist) { |
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| 640 | for (DomainComponent next : nextlist) { |
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| 641 | // add an edge to the graph |
||
| 642 | str += "\t" + curr.getName() + " -> " + next.getName(); |
||
| 643 | |||
| 644 | } |
||
| 645 | } |
||
| 646 | |||
| 647 | } |
||
| 648 | |||
| 649 | // close |
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| 650 | str += "\n}\n\n"; |
||
| 651 | |||
| 652 | try |
||
| 653 | { |
||
| 654 | File pdlFile = new File(FRAMEWORK_HOME + "hierarchy_graph.dot"); |
||
| 655 | try (BufferedWriter writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(pdlFile), "UTF-8"))) { |
||
| 656 | // write file |
||
| 657 | writer.write(str); |
||
| 658 | } |
||
| 659 | } |
||
| 660 | catch (Exception ex) { |
||
| 661 | throw new RuntimeException(ex.getMessage()); |
||
| 662 | } |
||
| 663 | } |
||
| 664 | |||
| 665 | |||
| 666 | /** |
||
| 667 | * |
||
| 668 | * @param graph |
||
| 669 | */ |
||
| 670 | private void exportDependencyGraph(Map<DomainComponent, Set<DomainComponent>> graph) |
||
| 671 | { |
||
| 672 | // export graph |
||
| 673 | String str = "digraph dependency_graph {\n"; |
||
| 674 | str += "\trankdir=TB;\n"; |
||
| 675 | str += "\tnode [fontsize=11, style=filled, fillcolor=azure, shape = box]\n"; |
||
| 676 | |||
| 677 | // check dependencies |
||
| 678 | for (DomainComponent comp : graph.keySet()) { |
||
| 679 | // check dependencies |
||
| 680 | for (DomainComponent dep : graph.get(comp)) { |
||
| 681 | // add an edge to the graph |
||
| 682 | str += "\t" + dep.getName() + " -> " + comp.getName(); |
||
| 683 | } |
||
| 684 | } |
||
| 685 | |||
| 686 | // close |
||
| 687 | str += "\n}\n\n"; |
||
| 688 | |||
| 689 | try |
||
| 690 | { |
||
| 691 | File pdlFile = new File(FRAMEWORK_HOME + "dependency_graph.dot"); |
||
| 692 | try (BufferedWriter writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(pdlFile), "UTF-8"))) { |
||
| 693 | // write file |
||
| 694 | writer.write(str); |
||
| 695 | } |
||
| 696 | } |
||
| 697 | catch (Exception ex) { |
||
| 698 | throw new RuntimeException(ex.getMessage()); |
||
| 699 | } |
||
| 700 | } |
||
| 701 | |||
| 702 | /** |
||
| 703 | * |
||
| 704 | * @param graph |
||
| 705 | */ |
||
| 706 | private void exportDecompositionGraph(Map<ComponentValue, List<List<ComponentValue>>> graph) |
||
| 707 | { |
||
| 708 | // export graph |
||
| 709 | String str = "digraph decomposition_graph {\n"; |
||
| 710 | str += "\trankdir=TB;\n"; |
||
| 711 | str += "\tnode [fontsize=11, style=filled, fillcolor=azure, shape = box]\n"; |
||
| 712 | |||
| 713 | // node id |
||
| 714 | int counter = 0; |
||
| 715 | // create AND nodes |
||
| 716 | int andCounter = 0; |
||
| 717 | // check the graph |
||
| 718 | for (ComponentValue value : graph.keySet()) |
||
| 719 | { |
||
| 720 | // check number of disjunctions |
||
| 721 | List<List<ComponentValue>> disjunctions = graph.get(value); |
||
| 722 | if (disjunctions.size() == 1) |
||
| 723 | { |
||
| 724 | String andNode = "AND_" + andCounter; |
||
| 725 | str += "\t" + andNode + " [fontsize=6, shape= oval, style=filled, fillcolor= palegreen];\n"; |
||
| 726 | |||
| 727 | str += "\t" + value.getComponent().getName() + "_" + value.getLabel().replace("-", "_") + |
||
| 728 | " -> " + andNode + ";\n"; |
||
| 729 | |||
| 730 | |||
| 731 | |||
| 732 | // set weight of the edge |
||
| 733 | Map<ComponentValue, Integer> wc = new HashMap<>(); |
||
| 734 | for (ComponentValue child : disjunctions.get(0)) { |
||
| 735 | if (!wc.containsKey(child)) { |
||
| 736 | wc.put(child, 1); |
||
| 737 | } |
||
| 738 | else { |
||
| 739 | // increment |
||
| 740 | int v = wc.get(child); |
||
| 741 | wc.put(child, ++v); |
||
| 742 | } |
||
| 743 | } |
||
| 744 | |||
| 745 | // no disjunctions |
||
| 746 | for (ComponentValue child : wc.keySet()) { |
||
| 747 | // add edge |
||
| 748 | str += "\t" + andNode + " -> " + child.getComponent().getName() + "_" + child.getLabel().replace("-", "_") + " [label= \"" + wc.get(child) + "\"];\n"; |
||
| 749 | } |
||
| 750 | |||
| 751 | // increment and node counter |
||
| 752 | andCounter++; |
||
| 753 | } |
||
| 754 | else |
||
| 755 | { |
||
| 756 | // add OR node label |
||
| 757 | String orLabel = "OR_" + counter; |
||
| 758 | // add an edge to the OR node |
||
| 759 | str += "\t" + orLabel + " [fontsize=6, shape= diamond, style=filled, fillcolor= thistle];\n"; |
||
| 760 | str += "\t" + value.getComponent().getName() + "_" + value.getLabel().replace("-", "_") + |
||
| 761 | " -> " + orLabel + ";\n"; |
||
| 762 | |||
| 763 | // add disjunctions |
||
| 764 | for (List<ComponentValue> conjunctions : disjunctions) |
||
| 765 | { |
||
| 766 | // set AND node label |
||
| 767 | String andLabel = "AND_" + andCounter; |
||
| 768 | str += "\t" + andLabel + " [fontsize=6, shape= oval, style=filled, fillcolor= palegreen];\n"; |
||
| 769 | |||
| 770 | // set weight of the edge |
||
| 771 | Map<ComponentValue, Integer> wc = new HashMap<>(); |
||
| 772 | for (ComponentValue child : conjunctions) { |
||
| 773 | if (!wc.containsKey(child)) { |
||
| 774 | wc.put(child, 1); |
||
| 775 | } |
||
| 776 | else { |
||
| 777 | // increment |
||
| 778 | int v = wc.get(child); |
||
| 779 | wc.put(child, ++v); |
||
| 780 | } |
||
| 781 | } |
||
| 782 | |||
| 783 | // add and edge to the AND node |
||
| 784 | str += "\t" + orLabel + " -> " + andLabel + ";\n"; |
||
| 785 | for (ComponentValue child : wc.keySet()) { |
||
| 786 | // add edge from AND node to the value |
||
| 787 | str += "\t" + andLabel + |
||
| 788 | " -> " + child.getComponent().getName() + "_" +child.getLabel().replace("-", "_") + " [label= \"" + wc.get(child) + "\"];\n"; |
||
| 789 | } |
||
| 790 | |||
| 791 | |||
| 792 | // increment and node counter |
||
| 793 | andCounter++; |
||
| 794 | } |
||
| 795 | |||
| 796 | counter++; |
||
| 797 | } |
||
| 798 | |||
| 799 | |||
| 800 | } |
||
| 801 | |||
| 802 | // close |
||
| 803 | str += "\n}\n\n"; |
||
| 804 | |||
| 805 | try |
||
| 806 | { |
||
| 807 | File pdlFile = new File(FRAMEWORK_HOME + "decomposition_graph.dot"); |
||
| 808 | try (BufferedWriter writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(pdlFile), "UTF-8"))) { |
||
| 809 | // write file |
||
| 810 | writer.write(str); |
||
| 811 | } |
||
| 812 | } |
||
| 813 | catch (Exception ex) { |
||
| 814 | throw new RuntimeException(ex.getMessage()); |
||
| 815 | } |
||
| 818 |
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If you really need to set this static field, consider writing a thread-safe setter and atomic getter.