How to Measure Software Redundancy
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- Winifred Clark
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1 How to Measure Software Redundancy Andrea Mattavelli University of Lugano, Switzerland In collaboration with: A. Carzaniga, M. Pezzè ( and A. Gorla, A. Goffi, N. Perino)
2 Software Redundancy
3 ... Software Redundancy Version 1 Input Checkpoint Execute Version Test Output Input Version 2 Selection Algorithm Output Exception Restore Fail Version n Yes Alternatives? No Fail N-version Recovery Blocks
4 ... Software Redundancy Deliberate Version 1 Input Checkpoint Execute Version Test Output Input Version 2 Selection Algorithm Output Exception Restore Fail Version n Yes Alternatives? No Fail N-version Recovery Blocks
5 Software Redundancy Intrinsic
6 Software Redundancy Intrinsic Google Guava MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value);
7 Software Redundancy Intrinsic Google Guava MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value));
8 Software Redundancy Intrinsic Joda-Time equivalences SWT GraphStream
9 Exploiting Intrinsic Redundancy Automatic Recovery From Runtime Failures Carzaniga, Gorla, Mattavelli, Perino, Pezzè [ICSE 2013] Cross-checking Oracles from Intrinsic Software Redundancy Goffi, Carzaniga, Gorla, Mattavelli, Pezzè [ICSE 2014]
10 Automatic Runtime Recovery A B Application state space
11 Automatic Runtime Recovery A Fault B Application state space
12 Automatic Runtime Recovery A Failure detection Fault B Application state space
13 Automatic Runtime Recovery A Checkpoint / Restore Fault Failure detection B Application state space
14 Automatic Runtime Recovery A Checkpoint / Restore Fault Failure detection Workaround B Application state space
15 Automatic Runtime Recovery Guava JodaTime Caliper Carrot2 Closure compiler FB2PDF Mutants Successfully recovered 27% 48% 43% 19%
16 Software Redundancy
17 Software Redundancy?
18 Software Redundancy? Intrinsic Google Guava MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); How much code do they share?
19 ... Software Redundancy? Deliberate Intrinsic Version 1 Google Guava Input Version 2 Version n Selection Algorithm Output MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); N-version Failures are correlated [Knight et al.] How much code do they share?
20 ... Software Redundancy? Deliberate Intrinsic Version 1 Google Guava Input Version 2 Version n Selection Algorithm Output MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); N-version How much redundancy is there?
21 ... Software Redundancy? Deliberate Intrinsic Version 1 Google Guava Input Version 2 Version n Selection Algorithm Output MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); N-version How much redundancy is there? Measuring Software Redundancy Carzaniga, Mattavelli, Pezzè [ICSE 2015]
22 Informal Definition of Redundancy Two fragments are redundant when they are functionally equivalent and at the same time their executions are different.
23 Informal Definition of Redundancy S0 CA Sn
24 CA
25 CA CB
26 CA Equivalence CB
27 CA Execution Diversity CB
28 Functional Equivalence S CA CB
29 Functional Equivalence
30 Observational Equivalence
31 Observational Equivalence CP OCP OCP CP
32 Observational Equivalence CP OCP OCP CP
33 Observational Equivalence CP2 CP3 CP4 CP OCP OCP2 OCP3 OCP4 OCP OCP2 OCP3 OCP4 CP CP2 CP3 CP4
34 Execution Diversity CA CB
35 Execution Diversity S CA Execution CA Execution CB CB
36 Execution Diversity Execution CA actiona actionb actionc actiond actionz actionb actionc actiond Execution CB
37 Execution Diversity Execution CA actiona actionb actionc actiond actionz actionb actionc actiond Execution CB
38 Software Redundancy Observational Equivalence Execution Diversity
39 Software Redundancy Observational Undecidable Equivalence Execution Diversity
40 Software Redundancy Observational Undecidable Equivalence Execution Diversity Binary measure Richer measure
41 A Practical Measure of Redundancy
42 A Practical Measure of Redundancy R= f ) Degree of Degree of Equivalence Diversity (,
43 A Practical Measure of Redundancy RS = es(ca,cb) ds(ca,cb) es, ds [0,1]
44 A Practical Measure of Redundancy RS = es(ca,cb) 0 ds(ca,cb) es, ds [0,1]
45 A Practical Measure of Redundancy RS = es(ca,cb) ds(ca,cb) 0 es, ds [0,1]
46 A Practical Measure of Redundancy RS = es(ca,cb) 1 ds(ca,cb) 1 es, ds [0,1]
47 A Practical Measure of Redundancy RS = es(ca,cb) ds(ca,cb) es, ds [0,1]
48 A Practical Measure of Redundancy RS = es(ca,cb) ds(ca,cb) es, ds [0,1] R CA,CB = AGGREGATE(RS)
49 A Practical Measure of Redundancy Sample the state space RS = es(ca,cb) ds(ca,cb) es, ds [0,1] R CA,CB = AGGREGATE(RS)
50 A Practical Measure of Redundancy Observational equivalence measure RS = es(ca,cb) ds(ca,cb) es, ds [0,1] R CA,CB = AGGREGATE(RS)
51 A Practical Measure of Redundancy RS = es(ca,cb) ds(ca,cb) es, ds [0,1] Difference between executions R CA,CB = AGGREGATE(RS)
52 A Practical Measure of Redundancy RS = es(ca,cb) ds(ca,cb) es, ds [0,1] R CA,CB = AGGREGATE(RS) Aggregate the redundancy measure
53 Sampling the State Space
54 Sampling the State Space ArrayListMultimap var0 = ArrayListMultimap.create(); var0.clear(); ArrayListMultimap var3 = ArrayListMultimap.create(); var3.clear(); boolean var5 = var3.isempty(); ArrayListMultimap var6 = ArrayListMultimap.create(); var6.clear(); boolean var8 = var6.isempty(); boolean var9 = var3.putall((multimap) var6); java.util.list var11 = var3.removeall("hi!"); boolean var12 = var0.putall((short) (-1), (java.lang.iterable) var11); var0.clear(); ArrayListMultimap var14 = ArrayListMultimap.create((Multimap) var0); ArrayListMultimap var17 = ArrayListMultimap.create(1, 10); var17.clear(); ArrayListMultimap var19 = ArrayListMultimap.create(); var19.clear(); ArrayListMultimap var21 = ArrayListMultimap.create((Multimap) var19); boolean var22 = var14.put(var17, var19);
55 Sampling the State Space ArrayListMultimap var0 = ArrayListMultimap.create(); var0.clear(); ArrayListMultimap var3 = ArrayListMultimap.create(); var3.clear(); boolean var5 = var3.isempty(); ArrayListMultimap var6 = ArrayListMultimap.create(); var6.clear(); boolean var8 = var6.isempty(); boolean var9 = var3.putall((multimap) var6); java.util.list var11 = var3.removeall("hi!"); boolean var12 = var0.putall((short) (-1), (java.lang.iterable) var11); var0.clear(); ArrayListMultimap var14 = ArrayListMultimap.create((Multimap) var0); ArrayListMultimap var17 = ArrayListMultimap.create(1, 10); var17.clear(); ArrayListMultimap var19 = ArrayListMultimap.create(); var19.clear(); ArrayListMultimap var21 = ArrayListMultimap.create((Multimap) var19); boolean var22 = var14.put(var17, var19); // Code fragment A
56 Observational Equivalence Measure ArrayListMultimap var0 = ArrayListMultimap.create(); var0.clear(); ArrayListMultimap var3 = ArrayListMultimap.create(); var3.clear(); boolean var5 = var3.isempty(); ArrayListMultimap var6 = ArrayListMultimap.create(); var6.clear(); boolean var8 = var6.isempty(); boolean var9 = var3.putall((multimap) var6); java.util.list var11 = var3.removeall("hi!"); boolean var12 = var0.putall((short) (-1), (java.lang.iterable) var11); var0.clear(); ArrayListMultimap var14 = ArrayListMultimap.create((Multimap) var0); ArrayListMultimap var17 = ArrayListMultimap.create(1, 10); var17.clear(); ArrayListMultimap var19 = ArrayListMultimap.create(); var19.clear(); ArrayListMultimap var21 = ArrayListMultimap.create((Multimap) var19); boolean var22 = var14.put(var17, var19); // Code fragment A
57 var0.clear(); ArrayListMultimap var14 = ArrayListMultimap.create((Multimap) var0); ArrayListMultimap var17 = ArrayListMultimap.create(1, 10); var17.clear(); ArrayListMultimap var19 = ArrayListMultimap.create(); var19.clear(); ArrayListMultimap var21 = ArrayListMultimap.create((Multimap) var19); boolean var22 = var14.put(var17, var19); // Code fragment A Observational Equivalence Measure
58 Observational Equivalence Measure // Code fragment A boolean var22 = var14.put(var17, var19); // linkage: boolean var22, ArrayListMultimap var14
59 Observational Equivalence Measure // Code fragment A // Code fragment B boolean var22 = var14.put(var17, var19); // linkage: boolean var22, ArrayListMultimap var14 List list = new List(); list.add(var19); boolean var22 = var14.putall(var17, list);
60 Observational Equivalence Measure // Code fragment A // Code fragment B boolean var22 = var14.put(var17, var19); // linkage: boolean var22, ArrayListMultimap var14 // generated probing code: System.out.println(var22); boolean x0 = var14.isempty(); System.out.println(x0); var14.clear(); java.util.map x1 = var14.asmap(); int x2 = var14.size(); System.out.println(x2); int x3 = x1.size(); System.out.println(x3); java.util.set x4 = x1.entryset(); java.util.iterator x5 = x4.iterator(); boolean x6 = x4.isempty(); System.out.println(x6); try { x5.remove(); } catch (java.lang.illegalstateexception e) { System.out.println(e); } //... probing code continues List list = new List(); list.add(var19); boolean var22 = var14.putall(var17, list);
61 Observational Equivalence Measure // Code fragment A // Code fragment B boolean var22 = var14.put(var17, var19); // linkage: boolean var22, ArrayListMultimap var14 List list = new List(); list.add(var19); boolean var22 = var14.putall(var17, list); true false 1 1 false // generated probing code: System.out.println(var22); boolean x0 = var14.isempty(); System.out.println(x0); var14.clear(); java.util.map x1 = var14.asmap(); int x2 = var14.size(); System.out.println(x2); int x3 = x1.size(); System.out.println(x3); java.util.set x4 = x1.entryset(); java.util.iterator x5 = x4.iterator(); boolean x6 = x4.isempty(); System.out.println(x6); try { x5.remove(); } catch (java.lang.illegalstateexception e) { System.out.println(e); } //... probing code continues true false 1 1 false
62 Observational Equivalence Measure es(ca,cb) = successful total CP1 CP2 CP3 CP4 CP5 CP6 CP7 CP8 CP9 CP10 es(ca,cb) = 1.0 CP1 CP2 CP3 CP4 CP5 CP6 CP7 CP8 CP9 CP10 es(ca,cb) = 0.7
63 Difference Between Executions ArrayListMultimap var0 = ArrayListMultimap.create(); var0.clear(); ArrayListMultimap var3 = ArrayListMultimap.create(); var3.clear(); boolean var5 = var3.isempty(); ArrayListMultimap var6 = ArrayListMultimap.create(); var6.clear(); boolean var8 = var6.isempty(); boolean var9 = var3.putall((multimap) var6); java.util.list var11 = var3.removeall("hi!"); boolean var12 = var0.putall((short) (-1), (java.lang.iterable) var11); var0.clear(); ArrayListMultimap var14 = ArrayListMultimap.create((Multimap) var0); ArrayListMultimap var17 = ArrayListMultimap.create(1, 10); var17.clear(); ArrayListMultimap var19 = ArrayListMultimap.create(); var19.clear(); ArrayListMultimap var21 = ArrayListMultimap.create((Multimap) var19); boolean var22 = var14.put(var17, var19);
64 ArrayListMultimap var14 = ArrayListMultimap.create((Multimap) var0); ArrayListMultimap var17 = ArrayListMultimap.create(1, 10); var17.clear(); ArrayListMultimap var19 = ArrayListMultimap.create(); var19.clear(); ArrayListMultimap var21 = ArrayListMultimap.create((Multimap) var19); boolean var22 = var14.put(var17, var19); Difference Between Executions
65 Difference Between Executions boolean var22 = var14.put(var17, var19); // Trace code fragment A Code Projections
66 Difference Between Executions boolean var22 = var14.put(var17, var19); // Trace code fragment A Code Projections Statement ArrayListMultimap.put(LObject;LObject;)Z@66 AbstractListMultimap.put(LObject;LObject;)Z@95 AbstractMultimap.put(LObject;LObject;)Z@200 Statement, Depth 3:ArrayListMultimap.put(LObject;LObject;)Z@66 4:AbstractListMultimap.put(LObject;LObject;)Z@95 5:AbstractMultimap.put(LObject;LObject;)Z@200
67 Difference Between Executions boolean var22 = var14.put(var17, var19); // Trace code fragment A Data Projections Type, Value Ljava/util/Map; {} Ljava/util/Set; [] Ljava/util/HashMap; {} I 1 I 1 Class, Field, Value AbstractMultimap.map {} HashMap.entrySet [] HashMap$EntrySet.this$0 {} HashMap$HashIterator.modCount 1 HashMap$HashIterator.expectedModCount 1
68 Difference Between Executions Code projection CA Code projection CB
69 Difference Between Executions Code projection CA Code projection CB ds(ca,cb) = 1-SIMILARITY(PS,A, PS,B)
70 A Practical Measure of Redundancy
71 A Practical Measure of Redundancy e s d s R s S S S S S S S S S S S
72 A Practical Measure of Redundancy e s d s R s S S S S S S S S S S S
73 A Practical Measure of Redundancy e s d s R s S S S S S S S S S S S
74 A Practical Measure of Redundancy e s d s R s S S S S S S S S S S S
75 A Practical Measure of Redundancy e s d s R s S S S S S S S S S S S R = AVG(Rs) = ± 0.10
76 Evaluation In theory, there is no difference between theory and practice. But, in practice, there is. - van de Snepscheut
77 Evaluation Is the proposed measure consistent? Are the measurements significant and useful?
78 Evaluation Is the proposed measure consistent? Are the measurements significant and useful?
79 Consistency: Stability
80 Consistency: Stability Ground Truth Algorithm # Impl. Binary search 4 Linear search 4 Bubble sort 7 Insertion sort 3 Merge sort 4 Quicksort 3
81 Redundancy Consistency: Stability Code Projections Extract to local variable Change name Inline expression Extract method Equivalent input ADice Man Cos DamLev Dice Euclid Jaccard Jaro JaroW Lev MC Need Ovlp qgrams SmithW SmithG Redundancy Data Projections Extract to local variable Change name Inline expression Extract method Equivalent input ADice Man Cos DamLev Dice Euclid Jaccard Jaro JaroW Lev MC Need Ovlp qgrams SmithW SmithG
82 Evaluation Is the proposed measure consistent? Are the measurements significant and useful?
83 Low-level vs High-level Code Redundancy vs Algorithmic Redundancy
84 Low-level vs High-level Code Redundancy vs Algorithmic Redundancy Binary search Linear search 1 1 Redundancy Redundancy DamLev Lev Need SmithW SmithG 0 DamLev Lev Need SmithW SmithG Same algorithm, different implementation Bubble sort Insertion sort 1 1 Redundancy Redundancy DamLev Lev Need SmithW SmithG 0 DamLev Lev Need SmithW SmithG
85 Low-level vs High-level Code Redundancy vs Algorithmic Redundancy Binary search Linear search 1 1 Redundancy Redundancy DamLev Lev Need SmithW SmithG 0 DamLev Lev Need SmithW SmithG Same algorithm, different implementation Bubble sort Insertion sort 1 1 Redundancy Redundancy DamLev Lev Need SmithW SmithG 0 DamLev Lev Need SmithW SmithG
86 Low-level vs High-level Code Redundancy vs Algorithmic Redundancy Binary search Linear search 1 1 Redundancy Redundancy DamLev Lev Need SmithW SmithG 0 DamLev Lev Need SmithW SmithG Different algorithm Bubble sort Insertion sort 1 1 Redundancy Redundancy DamLev Lev Need SmithW SmithG 0 DamLev Lev Need SmithW SmithG
87 Predictive Ability
88 Predictive Ability Automatic Runtime Recovery A Checkpoint / Restore Fault Failure detection Workaround B Application state space
89 Predictive Ability Automatic Runtime Recovery A Checkpoint / Restore Fault Failure detection Workaround B Application state space Does redundancy correlate with success?
90 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00
91 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00
92 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00
93 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00
94 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00
95 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00
96 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00
97 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00
98 Predictive Ability System Method (C A ) Workaround (C B ) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 Caliper LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00 Correlation: 0.94
99
100 ... Software Redundancy? Deliberate Intrinsic Version 1 Google Guava Input Version 2 Version n Selection Algorithm Output MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); N-version How much redundancy is there?
101 ... Software Redundancy? Informal Definition of Redundancy Deliberate Intrinsic Input Version 1 Version 2 Version n Selection Algorithm Output Google Guava MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); Two fragments are redundant when they are functionally equivalent and at the same time their executions are different. N-version How much redundancy is there?
102 ... Software Redundancy? Informal Definition of Redundancy A Practical Measure of Redundancy Deliberate Intrinsic Input Version 1 Version 2 Version n Selection Algorithm Output Google Guava MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); Two fragments are redundant when they are functionally equivalent and at the same time their executions are different. RS = es(ca,cb) ds(ca,cb) es, ds [0,1] N-version How much redundancy is there? R CA,CB = AGGREGATE(RS)
103 ... Software Redundancy? Informal Definition of Redundancy A Practical Measure of Redundancy Deliberate Intrinsic Input Version 1 Version 2 Version n Selection Algorithm Output Google Guava MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); Two fragments are redundant when they are functionally equivalent and at the same time their executions are different. RS = es(ca,cb) ds(ca,cb) es, ds [0,1] N-version How much redundancy is there? R CA,CB = AGGREGATE(RS) Redundancy Consistency: Stability Code Projections Extract to local variable Change name Inline expression Extract method Equivalent input ADice Man Cos DamLev Dice Euclid Jaccard Jaro JaroW Lev MC Need Ovlp qgrams SmithW SmithG Redundancy Data Projections Extract to local variable Change name Inline expression Extract method Equivalent input ADice Man Cos DamLev Dice Euclid Jaccard Jaro JaroW Lev MC Need Ovlp qgrams SmithW SmithG
104 ... Software Redundancy? Informal Definition of Redundancy A Practical Measure of Redundancy Deliberate Intrinsic Input Version 1 Version 2 Version n Selection Algorithm Output Google Guava MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); Two fragments are redundant when they are functionally equivalent and at the same time their executions are different. RS = es(ca,cb) ds(ca,cb) es, ds [0,1] N-version How much redundancy is there? R CA,CB = AGGREGATE(RS) Consistency: Stability Code Projections Low-level vs High-level Code Redundancy vs Algorithmic Redundancy Redundancy Extract to local variable Change name Inline expression Extract method Equivalent input ADice Man Cos DamLev Dice Euclid Jaccard Jaro JaroW Lev MC Need Ovlp qgrams SmithW SmithG Redundancy Binary search DamLev Lev Need SmithW SmithG Redundancy Linear search DamLev Lev Need SmithW SmithG Redundancy Data Projections Extract to local variable Change name Inline expression Extract method Equivalent input ADice Man Cos DamLev Dice Euclid Jaccard Jaro JaroW Lev MC Need Ovlp qgrams SmithW SmithG Redundancy Bubble sort DamLev Lev Need SmithW SmithG Different algorithm Redundancy Insertion sort DamLev Lev Need SmithW SmithG
105 ... Software Redundancy? Informal Definition of Redundancy A Practical Measure of Redundancy Deliberate Intrinsic Input Version 1 Version 2 Version n N-version Selection Algorithm Output Google Guava MultiMap m = new MultiMap(); // //add a key-value pair in the map m.put(key, value); m.putall(key, new List().add(value)); m.entryset().add(new Entry(key, value)); How much redundancy is there? Two fragments are redundant when they are functionally equivalent and at the same time their executions are different. RS = es(ca,cb) ds(ca,cb) R CA,CB es, ds [0,1] = AGGREGATE(RS) Consistency: Stability Code Projections Low-level vs High-level Code Redundancy vs Algorithmic Redundancy Predictive Ability Redundancy Redundancy Extract to local variable Change name Inline expression Extract method Equivalent input ADice Man Cos DamLev Dice Euclid Jaccard Jaro JaroW Lev MC Need Ovlp qgrams SmithW SmithG Data Projections Extract to local variable Change name Inline expression Extract method Equivalent input ADice Man Cos DamLev Dice Euclid Jaccard Jaro JaroW Lev MC Need Ovlp qgrams SmithW SmithG Redundancy Redundancy Binary search DamLev Lev Need SmithW SmithG Bubble sort DamLev Lev Need SmithW SmithG Redundancy Redundancy Different algorithm Linear search DamLev Lev Need SmithW SmithG Insertion sort DamLev Lev Need SmithW SmithG System Method (CA) Workaround (CB) Success ratio Redundancy Iterators.forArray(a) Arrays.asList(a).iterator() 3/3 (100%) 1.00 ± 0.00 LinkedHashMultiset.retainAll(Collection c) foreach(o in map) if(o not in c) map.remove(o); 1/2 (50%) 0.61 ±0.01 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 8/41 (20%) 0.37 ±0.32 Caliper LinkedHashMultimap.putAll(Object k, Collection c) foreach(o in c) put(k,o); 0/1 (0%) 0.00 ±0.00 LinkedHashMultimap.create() create(100,100) 0/207 (0%) 0.12 ±0.15 Caliper LinkedHashMultimap.create(int,int) create() 0/202 (0%) 0.12 ±0.15 LinkedHashMultimap.isEmpty() size() == 0? true : false 0/34 (0%) 0.00 ±0.00 ImmutableMultiset.of(Object..c) foreach(o in c) builder().setcount(o,count(o in c)) 13/22 (59%) 0.56 ±0.07 ImmutableMultiset.of(Object..c) builder().add(..c).build() 7/19 (37%) 0.24 ±0.12 ArrayListMultimap.putAll(Object k,collection c) foreach(o in c) put(k,o); 1/13 (8%) 0.37 ±0.32 ImmutableMultiset.of(Object o) builder().add(o).build() 0/1 (0%) 0.32 ±0.14 Lists.newArrayList() new ArrayList() 0/24 (0%) 0.00 ±0.00 Carrot Lists.newArrayList() new ArrayList(10) 0/24 (0%) 0.00 ±0.00 Lists.newArrayListWithCapacity(int c) new ArrayList() 0/20 (0%) 0.00 ±0.00 Carrot2 Lists.newArrayListWithCapacity(int c) new ArrayList(c) 0/20 (0%) 0.00 ±0.00 Maps.newHashMap() Maps.newHashMapWithExpectedSize(16) 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap() 0/54 (0%) 0.00 ±0.00 Maps.newHashMap() new HashMap(16) 0/54 (0%) 0.00 ±0.00 Correlation: 0.94
Measuring Software Redundancy
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