MACHINE LEARNING FOR SOFTWARE MAINTAINABILITY
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1 MACHINE LEARNING FOR SOFTWARE MAINTAINABILITY Anna Corazza, Sergio Di Martino, Valerio Maggio Alessandro Moschitti, Andrea Passerini, Giuseppe Scanniello, Fabrizio Silverstri JIMSE 2012 August 28, 2012 Montpellier, France
2 SOFTWARE MAINTENANCE A software system must be continuously adapted during its overall life cycle or it progressively becomes less satisfactory (cit. Lehman s Law of Software Evolution) Software Maintenance is one of the most expensive and time consuming phase of the whole life cycle Anticipating the Maintenance operations reduces the cost 85%-90% of the total cost are related to the effort necessary to comprehend the system and its source code [Erlikh, 2000]
3 SOFTWARE ARCHITECTURE Provide models and views representing the relationships among different software artifacts Application Facade Software Artifacts Service Interfaces Messages Interfaces Clustering of Software Artifacts UI Components Operational Management Business Components UI Process Components Data Access Components Data Helpers / Utilities Buisiness Workflows Advantages: Communications Security To aid the comprehension To reduce maintenance effort
4 SOFTWARE ARCHITECTURE Provide models and views representing the relationships among different software artifacts Clustering of Software Artifacts Advantages: Clusters of Software Artifacts Presentation Services Business UI Components UI Process Components Service Interfaces Buisiness Workflows Application Facade External Systems Service Consumers Messages Interfaces Business Components Cross Cutting Security Operational Management Communications To aid the comprehension Data Data Access Components Data Helpers / Utilities To reduce maintenance effort
5 SOFTWARE ARCHITECTURE Clusters of Software Artifacts Provide models and views representing the relationships among different software artifacts Presentation UI Components UI Process Components External Systems Service Consumers Cross Cutting Clustering of Software Artifacts Advantages: Business Services Service Interfaces Buisiness Workflows Messages Interfaces Application Facade Business Components Communications Operational Management Security To aid the comprehension Data Data Access Components Data Helpers / Utilities To reduce maintenance effort
6 SOFTWARE ARTIFACTS Software Artifacts may be analyzed at different levels of abstractions
7 SOFTWARE ARTIFACTS Software Artifacts may be analyzed at different levels of abstractions
8 SOFTWARE ARTIFACTS The different levels of abstractions lead to different analysis tasks: Software Artifacts may be analyzed at different levels of abstractions Identification of functional modules and their hierarchical arrangement i.e., Clustering of Software classes Identification of Code Clones i.e., Clustering of Duplicated code fragments (blocks,
9 SOFTWARE ARTIFACTS CLUSTERING Problem: Definition of a proper similarity measure to apply in the clustering analysis, which is able to exploit the considered representation of software artifacts Mine information directly from the source code: Exploit the syntactic/lexical information provided in the source code text Exploit the relational information between artifacts e.g., Program Dependencies
10 MINING LARGE REPOSITORIES Analysis of large and complex systems Solutions and algorithms must be able to scale efficiently (in the large and in the many)
11 ADVANCED MACHINE LEARNING FOR SOFTWARE MAINTENANCE Idea: Definition of Machine Learning techniques to mine information from the source code Combine different kind of information (lexical and structural) Application of Kernel Methods to software artifacts Provide flexible and computational effective solutions to analyze large data sets
12 ADVANCED MACHINE LEARNING FOR SOFTWARE MAINTENANCE Idea: Definition of Machine Learning techniques to mine information from the source code Combine different kind of information (lexical and structural) Application of Kernel Methods to software artifacts Provide flexible and computational effective solutions to analyze large data sets Advanced Machine Learning Learning with syntactic/semantic information (Natural Language Processing) Learning in relational domains (Structured-output learning, Logic Learning, Statistical Relational Learning)
13 KERNEL METHODS FOR STRUCTURED DATA A Kernel is a function between (arbitrary) pairs of entities It can be seen as a kind of similarity measure Based on the idea that structured objects can be described in terms of their constituent parts Generalize the computation of the dot product to arbitrary domains Can be easily tailored to specific domains Tree Kernels Graph Kernels...
14 KERNELS FOR STRUCTURES Computation of the dot product between (Graph) Structures
15 KERNELS FOR LANGUAGES Parse Trees represent the syntactic structure of a sentence Tree Kernels can be used to measure the similarity between parse trees
16 KERNELS FOR LANGUAGES Parse Trees represent the syntactic structure of a sentence Tree Kernels can be used to measure the similarity between parse trees KERNELS FOR SOURCE CODE Abstract Syntax Trees (AST) represent the syntactic structure of a piece of code Research on Tree Kernels for NLP carries over to AST (with adjustments)
17 KERNELS FOR PARSE TREE
18 KERNELS FOR AST
19 KERNEL MACHINES Idea: Any learning algorithm relying on similarity measure can be used Supervised Learning Binary Classification Multi-class Classification Ranking Unsupervised Learning Clustering Anomaly Detection
20 KERNEL MACHINES FOR CONE DETECTION Supervised Learning Pairwise classifier: predict if a pair of fragments is clone Unsupervised Learning Clustering: cluster together all candidate clones
21 KERNEL FOR CLONES
22 LEARNING SIMILARITIES KERNEL LEARNING Construct a number of candidate kernels with different characteristics e.g., Ignore variables names or not Employ kernel learning approaches which learn a weighted combination of candidate kernels Useless/harmful kernels will get zero weight and will be discarded in the final model
23 STRUCTURED-OUTPUT LEARNING Supervised Clustering Exploit information on already annotated pieces of software Training examples are software projects/portions with annotation on existing clones (clustering) A learning model uses training examples to refine the similarity measure for correctly clustering novel examples
24 SUMMARY Software has a rich structure and heterogeneous information Advanced Machine learning approaches are promising for exploiting such information Kernel Methods are natural candidate e.g., see the analogy between NLP parse trees and AST Many applications: architecture recovery, code clone detection, vulnerability detection...
25 CASE STUDY: KERNELS FOR CLONES
26 CODE CLONE DETECTION Goal: Identify and group all duplicated code fragments/functions Copy&Paste programming Taxonomy of 4 different types of clones Program Text similarities and Functional similarities Clones affect the reliability and the maintainability of a software system
27 KERNELS FOR CODE STRUCTURES CLONES Kernels for Structured Data: The source code could be represented by many different data structures Abstract Syntax Tree (AST) Tree structure representing the syntactic structure of the different instructions of a program (function) Program Dependencies Graph (PDG) (Directed) Graph structure representing the relationship among the different statement of a program
28 CODE STRUCTURES AST ABSTRACT SYNTAX TREE (AST) Function Body = print = while k 10 p i 0 < = AST embeds both Syntactic and Lexical Information Program Instructions Name of Variables, Literals... i 7 i + i 1.0
29 CODE STRUCTURES AST ABSTRACT SYNTAX TREE (AST) Function Body = print = while k 10 p i 0 < = AST embeds both Syntactic and Lexical Information Program Instructions Name of Variables, Literals... i 7 i + i 1.0
30 CODE STRUCTURES AST ABSTRACT SYNTAX TREE (AST) Function Body = print = while k 10 p i 0 < = AST embeds both Syntactic and Lexical Information Program Instructions Name of Variables, Literals... i 7 i + i 1.0
31 CODE STRUCTURES AST ABSTRACT SYNTAX TREE (AST) Function Body = print = while k 10 p i 0 < = AST embeds both Syntactic and Lexical Information Program Instructions Name of Variables, Literals... i 7 i + i 1.0
32 CODE STRUCTURES PDG PROGRAM DEPENDENCIES GRAPH (PDG) decl param decl while call-site Nodes correspond to instructions Edges represent relationships between couple of nodes arg
33 CODE STRUCTURES PDG PROGRAM DEPENDENCIES GRAPH (PDG) decl param decl while call-site Nodes correspond to instructions Edges represent relationships between couple of nodes arg
34 CODE STRUCTURES PDG PROGRAM DEPENDENCIES GRAPH (PDG) decl param decl while call-site Nodes correspond to instructions Edges represent relationships between couple of nodes arg
35 CODE STRUCTURES PDG PROGRAM DEPENDENCIES GRAPH (PDG) decl param decl while call-site Nodes correspond to instructions Edges represent relationships between couple of nodes arg
36 CODE STRUCTURES PDG PROGRAM DEPENDENCIES GRAPH (PDG) decl param decl while call-site Nodes correspond to instructions Edges represent relationships between couple of nodes arg
37 CODE STRUCTURES PDG NODES AND EDGES Two Types of Nodes Control Nodes (Dashed ones) e.g., if - for - while - function calls... Data Nodes while call-site e.g., essions - parameters... arg
38 CODE STRUCTURES PDG NODES AND EDGES Two Types of Nodes Control Nodes (Dashed ones) e.g., if - for - while - function calls... Data Nodes while call-site e.g., essions - parameters... Two Types of Edges (i.e., dependencies) arg Control edges (Dashed ones) Data edges
39 KERNELS FOR CODE STRUCTURES DEFINING KERNELS FOR STRUCTURED DATA The definition of a new Kernel for a Structured Object requires the definition of: Set of features to annotate each part of the object A Kernel function to measure the similarity on the smallest part of the object e.g., Nodes of AST and Graphs A Kernel function to apply the computation on the different (sub)parts of the structured object
40 KERNELS FOR CODE STRUCTURES: AST TREE KERNELS FOR AST Features: each node is characterized by a set of 4 features Instruction Class FOR i.e., LOOP, CONDITIONAL_STATEMENT, CALL Instruction i.e., FOR, IF, WHILE, RETURN FOR-INIT FOR- BODY Context i.e., Instruction Class of the closer statement node Lexemes Lexical information gathered (recursively) from leaves
41 KERNELS FOR CODE STRUCTURES: AST TREE KERNELS FOR AST Features: each node is characterized by a set of 4 features FOR Instruction Class = LOOP Instruction = FOR Context = (e.g., LOOP) Lexemes = (e.g, name of variables in FOR- INIT..) Instruction Class i.e., LOOP, CONDITIONAL_STATEMENT, CALL Instruction i.e., FOR, IF, WHILE, RETURN FOR-INIT FOR- BODY Context i.e., Instruction Class of the closer statement node Lexemes Lexical information gathered (recursively) from leaves
42 KERNELS FOR CODE STRUCTURES: AST TREE KERNELS FOR AST Goal: Identify the maximum isomorphic Tree/Subtree Comparison of blocks to each other Blocks: Atomic unit for (sub) tree considered BLOCK BLOCK = = print = = print s 0.0 p f p x 1.0 y f y s 1.0 x x
43 KERNELS FOR CODE STRUCTURES: AST TREE KERNELS FOR AST Goal: Identify the maximum isomorphic Tree/Subtree Comparison of blocks to each other Blocks: Atomic unit for (sub) tree considered BLOCK BLOCK = = print = = print s 0.0 p f p x 1.0 y f y s 1.0 x x
44 KERNELS FOR CODE STRUCTURES: PDG GRAPH KERNELS FOR PDG Features of nodes: Node Label while i.e.,, WHILE, CALL-SITE, EXPR,... Node Type i.e., Data Node or Control Node call-site Features of edges: arg Edge Type i.e., Data Edge or Control Edge
45 KERNELS FOR CODE STRUCTURES: PDG GRAPH KERNELS FOR PDG Features of nodes: while Node Label = WHILE Node Type = Control Node Node Label i.e.,, WHILE, CALL-SITE, EXPR,... Control Edge Node Type i.e., Data Node or Control Node call-site Data Edge Features of edges: arg Edge Type i.e., Data Edge or Control Edge
46 KERNELS FOR CODE STRUCTURES: PDG GRAPH KERNELS FOR PDG Goal: Identify common subgraphs Selectors: Compare nodes to each others and explore the subgraphs of only compatible nodes (i.e., Nodes of the same type) Context: The subgraph of a node (with paths whose lengths are at most L to avoid loops) while while call-site call-site call-site arg arg
47 KERNELS FOR CODE STRUCTURES: PDG GRAPH KERNELS FOR PDG Goal: Identify common subgraphs Selectors: Compare nodes to each others and explore the subgraphs of only compatible nodes (i.e., Nodes of the same type) Context: The subgraph of a node (with paths whose lengths are at most L to avoid loops) while while call-site call-site call-site arg arg
48 KERNELS FOR CODE STRUCTURES: PDG GRAPH KERNELS FOR PDG Goal: Identify common subgraphs Selectors: Compare nodes to each others and explore the subgraphs of only compatible nodes (i.e., Nodes of the same type) Context: The subgraph of a node (with paths whose lengths are at most L to avoid loops) while while call-site call-site call-site arg arg
49 KERNELS FOR CODE STRUCTURES: PDG GRAPH KERNELS FOR PDG Goal: Identify common subgraphs Selectors: Compare nodes to each others and explore the subgraphs of only compatible nodes (i.e., Nodes of the same type) Context: The subgraph of a node (with paths whose lengths are at most L to avoid loops) while while call-site call-site call-site arg arg
50 EVALUATION EMPIRICAL EVALUATION PROTOCOL Comparison of results with other two clone detector tools: AST-based Clone detector PDG-based Clone Detector
51 EVALUATION EMPIRICAL EVALUATION PROTOCOL Comparison of results with other two clone detector tools: AST-based Clone detector PDG-based Clone Detector No publicly available clone detection dataset
52 EVALUATION EMPIRICAL EVALUATION PROTOCOL Comparison of results with other two clone detector tools: AST-based Clone detector PDG-based Clone Detector No publicly available clone detection dataset No unique set of analyzed open source systems
53 EVALUATION EMPIRICAL EVALUATION PROTOCOL Comparison of results with other two clone detector tools: AST-based Clone detector PDG-based Clone Detector No publicly available clone detection dataset No unique set of analyzed open source systems Usually clone results are not available
54 EVALUATION EMPIRICAL EVALUATION PROTOCOL Comparison of results with other two clone detector tools: AST-based Clone detector PDG-based Clone Detector No publicly available clone detection dataset No unique set of analyzed open source systems Usually clone results are not available Two possible strategies:
55 EVALUATION EMPIRICAL EVALUATION PROTOCOL Comparison of results with other two clone detector tools: AST-based Clone detector PDG-based Clone Detector No publicly available clone detection dataset No unique set of analyzed open source systems Usually clone results are not available Two possible strategies: To automatically modify an existing system with randomly generated clones
56 EVALUATION EMPIRICAL EVALUATION PROTOCOL Comparison of results with other two clone detector tools: AST-based Clone detector PDG-based Clone Detector No publicly available clone detection dataset No unique set of analyzed open source systems Usually clone results are not available Two possible strategies: To automatically modify an existing system with randomly generated clones Manual classification of candidate results
57 BENCHMARKS AND EMPIRICAL EVALUATION DATASET Project Size (KLOC) # PDGS Apache Python Comparison with another Graph-based clone detector MeCC (ICSE2011) Baseline Dataset Results provided by MeCC Extended Dataset Extension of Clones results by manual evaluation of candidate clones Agreement rate calculation between the evaluators
58 EVALUATION TREE KERNELS FOR AST EMPIRICAL EVALUATION OF TREE KERNEL FOR AST Comparison with another (pure) AST-based clone detector Clone Digger Comparison on a system with randomly seeded clones Results refer to clones where code fragments have been modified by adding/removing or changing code statements
59 EVALUATION TREE KERNELS FOR AST PRECISION, RECALL AND F1 PLOT Clone results with different similarity thresholds Precision Recall F1
60 EVALUATION LOREM GRAPH KERNELS I PFOR SPDGU M RESULTS WITH APACHE Threshold #Clones in the Baseline #Clones in the Extended Dataset " 0.6" 0.8" 0.7" 0.5" 0.6" 0.4" 0.5" 0.4" 0.3" 0.3" 0.2" 0.2" 0.1" 0" 0.99" 1" Soglia'di'Similarità' Precision5 Baseline" Precision5 Extended" 0.1" 0" 0.99" 1" Soglia'di'Similarità' Recall0 Baseline" Recall0 Extended"
61 EVALUATION LOREM GRAPH KERNELS I PFOR SPDGU M RESULTS WITH PYTHON Threshold #Clones in the Baseline #Clones in the Extended Dataset " 0.9" 1" 0.8" 0.7" 0.8" 0.6" 0.6" Precision2 Baseline" 0.5" 0.4" Recall2Baseline" Recall2Extended" 0.4" Precision2 Extended" 0.3" 0.2" 0.2" 0.1" 0" 0.99" 1" Soglia'di'Similarità' 0" 0.99" 1" Soglia'di'Similarità'
62 CHALLENGES AND OPPORTUNITIES Learning Kernel Functions from Data Set Kernel Methods advantages: flexible solution to be tailored to specific domain efficient solution easy to parallelize combinations of multiple kernels Provide a publicly available data set
63 THANK YOU FOR YOUR KIND ATTENTION
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