大佬講MBT
網址：https://www.youtube.com/watch?v=TnuiEGS6iyc
獲取模型：
1.手工創建（Manual creation）
2.基於事件接口（Event based inference）
- Apply regular language learning algorithms to traces
- Minimize over/under approximation through multi objective optimization
2012 ICST-Finding the Optimal Balance between Over and Under Approximation of Models Inferred from Execution Logs.
Grammer learning:Algorithms to learn a regular language from its sentences(e.g.,k-tail)
兩個問題：Over-approximation:behaviors that are admitted by the model,but are impossible in reality最小化模型尺寸
Under-approximation:behaviours that are possible,but are not admitted by the model 多目標優化算法
Metrics:SIZE,UNOBS,UNREC
unobserved even sequences and a small number of unrecognized traces
take all the traces that are available and to put them in parallel as an only mystic choice among them if you do that
多目標優化
select non dominated models->update paretofront->Model union/intersection
Add trace/merge states
we cycle and after a number of iterations we get minimized
Model union & intersection
Non-deterministic union/union+powerset construction/intersection
可以將模型轉換爲非確定有限自動機轉換爲確定
3.基於狀態接口（Stated based inference）FSM
- Manually define the abstraction function
- Infer the abstraction function through clustering and multi-objective optimization
Automated Generation of stated abstraction functions using data invariant inference.AST-2013
ReAjax:a review engineering tool for Ajax Web applications.2012.
State clustering -> Invariant inference
Automated generation based on clustering & data invariant inference
Partitioning problem
Given a set of concrete states,find a partition such that the invariants obtained from each subset in the partition produce an optimal model when used as abstraction functions
減少模型尺寸，減少非確定性轉換
select non dominated partitions->update pareto front->apply genetic operators
Evolving the model
1.Manual evolution
2.Inference from scratch,using the new traces
3.Automated,incremental model update
a new state and new transitions are added to the model
Revolution : Atumatic Evolution of Mined Specifications.2012
Using the model
1.test sequence generation
- Random,depth-first,breadth first
- Semantic interactions
- Maximum diversity
BFS/DFS First Algorithm
the notion of semantic interactions
<text,submit >
<submit,text>
<subject,submit>
<submit,subject>
the coverage of pairs of events interact semantically
test suite diversity
Frequency based diversity is maximized
Distance metrics:minimum Euclideon distance between event execution frequencies of test cases or sum of distances from the average frequency
DIV多樣性矩陣
2.test data generation
TC generator -> FSM monitor -> SUT
Transition coverage onModel is satisfied iff branch coverage on FSM Monitor is satisfied
Test generator: Any test case generator supporting branch coverage(Evosuite)
combinatorial input data generation
Each pairwise combination is covered by at least one test sequence
infeasible test sequences
Finite State Models express an abstraction of the semantics
- Not all dependencies and constraints are represented
- Test cases may violate missing dependencies and constraints
3.test case feasibility
infeasibility can cause a lot of trouble in the actual execution of tests against the application it can reduce coverage and for detection substantially and at end it invovle substantial manual effort so
Predicting feasible test sequences
In NLP,N-grams are used for word prediction
In MBT,N-grams can be used to predict the next feasible event in a test sequence.
Test case derivation:2-Grams
Interpolated N-Grams for Model Based TestingICSE
Defining the oracle
1.Manual definition
Add assertions to express:
- Contracts(pre/post conditions,invariants)
- Expected test case results
2.Model as oracle
TC=<add,add,empty>
- Expected SUT state should match actual SUT state
3.Crash oracle
4.Automated oracles
anomaly detectors
圖的遍歷產生測試序列
N-gram based test sequence generation from finite state models.