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Test Routine Automation through Natural
Language Processing Techniques
Voulivasi Triantafyllia
Guided by:
A. Symeonidis, Aristotle University of Thessaloniki
E. Gómez, European Space Operations Centre
11 July 2017
ARISTOTLE UNIVERSITY OF THESSALONIKI
Faculty of Engineering
Department of Electrical and Computer Engineering
Information Processing Laboratory
1. Introduction
2. Outcomes
3. Background
4. Methodology
5. Experiments
6. Conclusion & Future Work
AGENDA
1. Introduction
2. Outcomes
3. Background
4. Methodology
5. Experiments
6. Conclusion & Future Work
AGENDA
Introduction
● Ground Segment is a SoS
(System of Systems)
● Testing is complicated
● Requires well-established testing
infrastructure
Objectives
1. Automation in the creation of tests
2. Requirements Tracing
Objectives
1. Automation in the creation of tests
● Time-consuming procedure: Senior SE → Tester
● Difficult to work with for new testers
2. Requirements Tracing
Objectives
1. Automation in the creation of tests
● Time-consuming procedure: Senior SE → Tester
● Difficult to work with for new testers
2. Requirements Tracing
● Tests originate from SPRs (Software Problem Report) instead of
requirements
● Lack of consistency in evaluation of software requirements
Ground Segment Testing Infrastructure
Ground Segment Testing Infrastructure
Ground Segment Testing Infrastructure
Ground Segment Testing Infrastructure
Test Scenario
Test Scenario
Natural Language
Automated test
Sequence of test building blocks
Automated test
Sequence of test building blocks
Test Block Attributes:
● Name
● Description
● Parameters
● Precondition
● Postcondition
Automated test
Sequence of test building blocks
Test Block Attributes:
● Name
● Description
● Parameters
● Precondition
● Postcondition
Natural Language
Software Requirement
“The SCOS-2000 environment should warn for
any parameters with Out-Of-Limit values.”
Software Requirement
“The SCOS-2000 environment should warn for
any parameters with Out-Of-Limit values.”
Natural Language
1. Introduction
2. Outcomes
3. Background
4. Methodology
5. Experiments
6. Conclusion & Future Work
AGENDA
Triantafyllia Voulibasi
Triantafyllia Voulibasi
High-Level System Design
High-Level System Design
Information Retrieval
Natural Language Processing
Word Embeddings
Recommender Systems
Association Rules
1. Introduction
2. Outcomes
3. Background
4. Methodology
5. Experiments
6. Conclusion & Future Work
AGENDA
Natural Language Representation
?Natural
Language
Machine
Representation
Natural Language Representation
Vector Space
Model
Natural
Language
Machine
Representation
Natural Language Representation
Vector Space
Model
Natural
Language
Machine
Representation
capital 195
Natural Language Representation
Vector Space
Model
Natural
Language
Machine
Representation
One-hot encoding
● Does not capture semantics
● Huge Length
-- equal to the size of the total unique
vocabulary in the corpora
capital 195
Natural Language Representation
Vector Space
Model
Natural
Language
Machine
Representation
One-hot encoding
● Does not capture semantics
● Huge Length
-- equal to the size of the total unique
vocabulary in the corpora
capital 195
Word Embeddings
● state-of-the-art word embedding methods: Word2Vec, Glove and FastText changed
completely NLP
● reduce dimensionality
● capture semantics
Word2Vec vector:
[0.12, 0.23, 056]
Word2vec: simplified structure
● Shallow feed-forward neural network with one hidden layer and linear activation function
● Input and output are hot-encoded vectors of pairs of words: drink | juice, New | York
● The word vectors are referring to the first (left) weight matrix
Word2vec: simplified structure
● Shallow feed-forward neural network with one hidden layer and linear activation function
● Input and output are hot-encoded vectors of pairs of words: drink | juice, New | York
● The word vectors are referring to the first (left) weight matrix
Word2Vec architectures
Skip-gram CBOW
Word2Vec architectures
Word2Vec Parameters
● size A value of 100 - 1000 for the dimension of the hidden layer
● window The maximum distance between the target word and a
neighbor word
● min_count Minimum frequency count of words
● workers How many threads to use behind the scenes?
● sg Whether to use skip-gram or CBOW architecture
● negative Whether to use negative sampling
● corpus relevant documents
Word2Vec Parameters
● size A value of 100 - 1000 for the dimension of the hidden layer
● window The maximum distance between the target word and a
neighbor word
● min_count Minimum frequency count of words
● workers How many threads to use behind the scenes?
● sg Whether to use skip-gram or CBOW architecture
● negative Whether to use negative sampling
● corpus relevant documents
Word2Vec Parameters
● size A value of 100 - 1000 for the dimension of the hidden layer
● window The maximum distance between the target word and a
neighbor word
● min_count Minimum frequency count of words
● workers How many threads to use behind the scenes?
● sg Whether to use skip-gram or CBOW architecture
● negative Whether to use negative sampling
● corpus relevant documents
Word2Vec Corpus: Software Documentation
● Glossary ● Software Problem Report
● Technical Notes ● Software Design Document
● Software Development Plan ● Kick Off Meeting Minutes
● Software User Manual ● Final Report
● Software Requirements Specification ● Software Validation Specification
● Software Unit and Integration Test
Plan
● Configuration and Installation Guide
Text Similarity
Consider sentences A, B:
A contains words:
B contains words:
Text Similarity
sentence representations:
Consider sentences A, B:
A contains words:
B contains words:
Text Similarity
sentence representations:
Consider sentences A, B:
A contains words:
B contains words:
Text Similarity: other approaches
Jaccard Index
TF-IDF
LSI
1. Introduction
2. Outcomes
3. Background
4. Methodology
5. Experiments
6. Conclusion & Future Work
AGENDA
High-Level System Design
Triantafyllia Voulibasi
Triantafyllia Voulibasi
Spell Checker
Spell Checker
Levenshtein distance (LD)
s = "test"
t = "test" → LD(s,t) = 0
no transformations are needed
s = "test"
t = "tent" → LD(s,t) = 1
one substitution transforms s into t
Presenter
● Present the processed information to the
UI
● Communicate with the other components
to trigger data processing in the system
Presenter & Parser
Parser
● Identify how many blocks match to a
sentence
● Identify a test step’s category
1. Informative
“Open the Manual Stack and disable
dynamic PTV checks.” → 2 test blocks
2. Repetitive
“Repeat steps 1 to 4.” → 4 test blocks
NLP Filter
Recommender
1. Compute similarities:
2. Improve recommendations from user feedback:
Recommender - Keywords
step keywords:
block keywords:
Recommender - Parameters
Step Parameters Block
Parameters
Parameter
Score
a, b, c a, b, c 1.0
- a, b, c 0.0
a, b, c - 0.0
a, b, c a, c 0.6667
Recommender - Association Rules Mining
● Re-ranking based on Association Rules Mining
Itemsets of interest: the previous blocks and the
block in question
● Calculate all those itemsets and the number of their
occurences together σ: support count
● Calculate the confidence scores c of X → Y
○ X : the previous blocks
○ Y : each test block in question
ID Items
1 {1, 2}
2 {1, 2, 3, 4}
3 {1, 2, 3 5}
Recommender - Association Rules Mining
● Re-ranking based on Association Rules Mining
Itemsets of interest: the previous blocks and the
block in question
● Calculate all those itemsets and the number of their
occurences together σ: support count
● Calculate the confidence scores c of X → Y
○ X : the previous blocks
○ Y : each test block in question
ID Items
1 {1, 2}
2 {1, 2, 3, 4}
3 {1, 2, 3 5}
Flow Checker & Data Container
Flow Checker Data Container
● data import
● data export
● data distribution to other components
(i.e. Repetitive category test step)
1. Introduction
2. Outcomes
3. Background
4. Methodology
5. Experiments
6. Conclusion & Future Work
AGENDA
Dataset
Word2Vec Training Corpus
ESA Mission Control System Infrastructure
● Files: ~200
● Requirements: 5569
● Test Scenarios: 5040
Word counts: 3.006.330
Word embeddings: 31580
Testing Dataset
● Requirements: 5569
● Test Blocks
○ Libraries: 21
○ Extracted Test Blocks: 2160
○ Filtered - High Level Test Blocks: 685
● Test Scenarios
○ Automated by a test engineer: 8
○ Total Test Steps: 181
○ Associated Test Blocks: 260
○ Linked Requirements: 36
Evaluation Measures - Precision And Recall
Evaluation Measures
Word2Vec Models
Vector Space Models
Test Blocks Requirements
User Feedback
Efficiency and Productivity
78.8%
Automated Test
Creation
57.4%
Requirements
Tracing
<0.1 sec
Automated Test
Creation
<1 sec
Requirements
Tracing
1. Introduction
2. Outcomes
3. Background
4. Methodology
5. Experiments
6. Conclusion & Future Work
AGENDA
● This work is the first step towards AI of SWE data in ESOC
● Retrieve software documentation information
● Increase productivity of the Testing team
● Hidden purpose: gather labeled data
Conclusion
● This work is the first step towards AI of SWE data in ESOC
● Retrieve software documentation information
● Increase productivity of the Testing team
● Hidden purpose: gather labeled data
Future Work
● Use of a Deep Learning Model for recommendations
● Embed pre-trained Word vectors
● On-site experiments in time and effort
● Incorporate Software Testing metrics
Conclusion
Thanks to:
● Assoc. Professor Andreas Symeonidis
● Eduardo Gómez
● ESOC Data Analytics team
● ISSEL Labgroup
Thank you for your attention.
Questions?

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ΣΧΕΔΙΑΣΗ ΚΑΙ ΑΝΑΠΤΥΞΗ ΜΗΧΑΝΙΣΜΟΥ ΑΥΤΟΜΑΤΟΠΟΙΗΣΗΣ ΤΗΣ ΑΝΑΠΤΥΞΗΣ ΓΡΑΦΙΚΩΝ ΕΝΤΟΛ...
 

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Triantafyllia Voulibasi

  • 1. Test Routine Automation through Natural Language Processing Techniques Voulivasi Triantafyllia Guided by: A. Symeonidis, Aristotle University of Thessaloniki E. Gómez, European Space Operations Centre 11 July 2017 ARISTOTLE UNIVERSITY OF THESSALONIKI Faculty of Engineering Department of Electrical and Computer Engineering Information Processing Laboratory
  • 2. 1. Introduction 2. Outcomes 3. Background 4. Methodology 5. Experiments 6. Conclusion & Future Work AGENDA
  • 3. 1. Introduction 2. Outcomes 3. Background 4. Methodology 5. Experiments 6. Conclusion & Future Work AGENDA
  • 4. Introduction ● Ground Segment is a SoS (System of Systems) ● Testing is complicated ● Requires well-established testing infrastructure
  • 5. Objectives 1. Automation in the creation of tests 2. Requirements Tracing
  • 6. Objectives 1. Automation in the creation of tests ● Time-consuming procedure: Senior SE → Tester ● Difficult to work with for new testers 2. Requirements Tracing
  • 7. Objectives 1. Automation in the creation of tests ● Time-consuming procedure: Senior SE → Tester ● Difficult to work with for new testers 2. Requirements Tracing ● Tests originate from SPRs (Software Problem Report) instead of requirements ● Lack of consistency in evaluation of software requirements
  • 8. Ground Segment Testing Infrastructure
  • 9. Ground Segment Testing Infrastructure
  • 10. Ground Segment Testing Infrastructure
  • 11. Ground Segment Testing Infrastructure
  • 14. Automated test Sequence of test building blocks
  • 15. Automated test Sequence of test building blocks Test Block Attributes: ● Name ● Description ● Parameters ● Precondition ● Postcondition
  • 16. Automated test Sequence of test building blocks Test Block Attributes: ● Name ● Description ● Parameters ● Precondition ● Postcondition Natural Language
  • 17. Software Requirement “The SCOS-2000 environment should warn for any parameters with Out-Of-Limit values.”
  • 18. Software Requirement “The SCOS-2000 environment should warn for any parameters with Out-Of-Limit values.” Natural Language
  • 19. 1. Introduction 2. Outcomes 3. Background 4. Methodology 5. Experiments 6. Conclusion & Future Work AGENDA
  • 23. High-Level System Design Information Retrieval Natural Language Processing Word Embeddings Recommender Systems Association Rules
  • 24. 1. Introduction 2. Outcomes 3. Background 4. Methodology 5. Experiments 6. Conclusion & Future Work AGENDA
  • 26. Natural Language Representation Vector Space Model Natural Language Machine Representation
  • 27. Natural Language Representation Vector Space Model Natural Language Machine Representation capital 195
  • 28. Natural Language Representation Vector Space Model Natural Language Machine Representation One-hot encoding ● Does not capture semantics ● Huge Length -- equal to the size of the total unique vocabulary in the corpora capital 195
  • 29. Natural Language Representation Vector Space Model Natural Language Machine Representation One-hot encoding ● Does not capture semantics ● Huge Length -- equal to the size of the total unique vocabulary in the corpora capital 195
  • 30. Word Embeddings ● state-of-the-art word embedding methods: Word2Vec, Glove and FastText changed completely NLP ● reduce dimensionality ● capture semantics Word2Vec vector: [0.12, 0.23, 056]
  • 31. Word2vec: simplified structure ● Shallow feed-forward neural network with one hidden layer and linear activation function ● Input and output are hot-encoded vectors of pairs of words: drink | juice, New | York ● The word vectors are referring to the first (left) weight matrix
  • 32. Word2vec: simplified structure ● Shallow feed-forward neural network with one hidden layer and linear activation function ● Input and output are hot-encoded vectors of pairs of words: drink | juice, New | York ● The word vectors are referring to the first (left) weight matrix
  • 35. Word2Vec Parameters ● size A value of 100 - 1000 for the dimension of the hidden layer ● window The maximum distance between the target word and a neighbor word ● min_count Minimum frequency count of words ● workers How many threads to use behind the scenes? ● sg Whether to use skip-gram or CBOW architecture ● negative Whether to use negative sampling ● corpus relevant documents
  • 36. Word2Vec Parameters ● size A value of 100 - 1000 for the dimension of the hidden layer ● window The maximum distance between the target word and a neighbor word ● min_count Minimum frequency count of words ● workers How many threads to use behind the scenes? ● sg Whether to use skip-gram or CBOW architecture ● negative Whether to use negative sampling ● corpus relevant documents
  • 37. Word2Vec Parameters ● size A value of 100 - 1000 for the dimension of the hidden layer ● window The maximum distance between the target word and a neighbor word ● min_count Minimum frequency count of words ● workers How many threads to use behind the scenes? ● sg Whether to use skip-gram or CBOW architecture ● negative Whether to use negative sampling ● corpus relevant documents
  • 38. Word2Vec Corpus: Software Documentation ● Glossary ● Software Problem Report ● Technical Notes ● Software Design Document ● Software Development Plan ● Kick Off Meeting Minutes ● Software User Manual ● Final Report ● Software Requirements Specification ● Software Validation Specification ● Software Unit and Integration Test Plan ● Configuration and Installation Guide
  • 39. Text Similarity Consider sentences A, B: A contains words: B contains words:
  • 40. Text Similarity sentence representations: Consider sentences A, B: A contains words: B contains words:
  • 41. Text Similarity sentence representations: Consider sentences A, B: A contains words: B contains words:
  • 42. Text Similarity: other approaches Jaccard Index TF-IDF LSI
  • 43. 1. Introduction 2. Outcomes 3. Background 4. Methodology 5. Experiments 6. Conclusion & Future Work AGENDA
  • 48. Spell Checker Levenshtein distance (LD) s = "test" t = "test" → LD(s,t) = 0 no transformations are needed s = "test" t = "tent" → LD(s,t) = 1 one substitution transforms s into t
  • 49. Presenter ● Present the processed information to the UI ● Communicate with the other components to trigger data processing in the system Presenter & Parser Parser ● Identify how many blocks match to a sentence ● Identify a test step’s category 1. Informative “Open the Manual Stack and disable dynamic PTV checks.” → 2 test blocks 2. Repetitive “Repeat steps 1 to 4.” → 4 test blocks
  • 51. Recommender 1. Compute similarities: 2. Improve recommendations from user feedback:
  • 52. Recommender - Keywords step keywords: block keywords:
  • 53. Recommender - Parameters Step Parameters Block Parameters Parameter Score a, b, c a, b, c 1.0 - a, b, c 0.0 a, b, c - 0.0 a, b, c a, c 0.6667
  • 54. Recommender - Association Rules Mining ● Re-ranking based on Association Rules Mining Itemsets of interest: the previous blocks and the block in question ● Calculate all those itemsets and the number of their occurences together σ: support count ● Calculate the confidence scores c of X → Y ○ X : the previous blocks ○ Y : each test block in question ID Items 1 {1, 2} 2 {1, 2, 3, 4} 3 {1, 2, 3 5}
  • 55. Recommender - Association Rules Mining ● Re-ranking based on Association Rules Mining Itemsets of interest: the previous blocks and the block in question ● Calculate all those itemsets and the number of their occurences together σ: support count ● Calculate the confidence scores c of X → Y ○ X : the previous blocks ○ Y : each test block in question ID Items 1 {1, 2} 2 {1, 2, 3, 4} 3 {1, 2, 3 5}
  • 56. Flow Checker & Data Container Flow Checker Data Container ● data import ● data export ● data distribution to other components (i.e. Repetitive category test step)
  • 57. 1. Introduction 2. Outcomes 3. Background 4. Methodology 5. Experiments 6. Conclusion & Future Work AGENDA
  • 58. Dataset Word2Vec Training Corpus ESA Mission Control System Infrastructure ● Files: ~200 ● Requirements: 5569 ● Test Scenarios: 5040 Word counts: 3.006.330 Word embeddings: 31580 Testing Dataset ● Requirements: 5569 ● Test Blocks ○ Libraries: 21 ○ Extracted Test Blocks: 2160 ○ Filtered - High Level Test Blocks: 685 ● Test Scenarios ○ Automated by a test engineer: 8 ○ Total Test Steps: 181 ○ Associated Test Blocks: 260 ○ Linked Requirements: 36
  • 59. Evaluation Measures - Precision And Recall
  • 62. Vector Space Models Test Blocks Requirements
  • 64. Efficiency and Productivity 78.8% Automated Test Creation 57.4% Requirements Tracing <0.1 sec Automated Test Creation <1 sec Requirements Tracing
  • 65. 1. Introduction 2. Outcomes 3. Background 4. Methodology 5. Experiments 6. Conclusion & Future Work AGENDA
  • 66. ● This work is the first step towards AI of SWE data in ESOC ● Retrieve software documentation information ● Increase productivity of the Testing team ● Hidden purpose: gather labeled data Conclusion
  • 67. ● This work is the first step towards AI of SWE data in ESOC ● Retrieve software documentation information ● Increase productivity of the Testing team ● Hidden purpose: gather labeled data Future Work ● Use of a Deep Learning Model for recommendations ● Embed pre-trained Word vectors ● On-site experiments in time and effort ● Incorporate Software Testing metrics Conclusion
  • 68. Thanks to: ● Assoc. Professor Andreas Symeonidis ● Eduardo Gómez ● ESOC Data Analytics team ● ISSEL Labgroup
  • 69. Thank you for your attention. Questions?