Adaptive Static Analysis via Learning with Bayesian Optimization
نویسندگان
چکیده
ions are binary vectors with indices in JP , and are ordered pointwise: a v a′ ⇐⇒ ∀j ∈ JP . aj ≤ aj . Intuitively, JP consists of the parts of P where we have switches for controlling the precision of an analysis. For instance, in a partially context-sensitive analysis, JP is the set of procedures or call sites in the program. In our partially ow-sensitive analysis, it denotes the set of program variables that are analyzed ow-sensitively. An abstraction a is just a particular seing of the switches associated with JP , and determines a program abstraction to be used by the analyzer. us, aj = 1 means that the component j ∈ JP is analyzed, e.g., with context sensitivity or ow sensitivity. We sometimes regard an abstraction a ∈ AP as a function from JP to {0, 1}, or the following collection of P ’s parts: a = {j ∈ JP | aj = 1}. In the laer case, we write |a| for the size of the collection. e last notation is two constants in AP : 0 = λj ∈ JP . 0, and 1 = λj ∈ JP . 1, 2is type of an analysis is usually called parametric program analysis [15]. We do not use this phrase in the paper to avoid confusion; if we did, we would have two types of parameters, ones for selecting parts of a given program, and the others for deciding a particular adaption strategy of the analysis. ACM Transactions on Programming Languages and Systems, Vol. 1, No. 1, Article 1. Publication date: January 2016. Adaptive Static Analysis via Learning with Bayesian Optimization 1:9 which represent the most imprecise and precise abstractions, respectively. In the rest of this paper, we omit the subscript P when there is no confusion. We assume that a set of assertions is given together with P . e goal of the analysis is to prove as many assertions as possible. An adaptive static analysis is modeled as a function:
منابع مشابه
Comparative Analysis of Machine Learning Algorithms with Optimization Purposes
The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches. Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data. In this paper, a methodology has been employed to opt...
متن کاملPractical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Computational models in fields such as computational neuroscience are often evaluated via stochastic simulation or numerical approximation. Fitting these models implies a difficult optimization problem over complex, possibly noisy parameter landscapes. Bayesian optimization (BO) has been successfully applied to solving expensive black-box problems in engineering and machine learning. Here we ex...
متن کامل Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization
A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this letter. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survi...
متن کاملLearning Bayesian Network Structure Using Genetic Algorithm with Consideration of the Node Ordering via Principal Component Analysis
‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...
متن کاملSTATIC AND DYNAMIC OPPOSITION-BASED LEARNING FOR COLLIDING BODIES OPTIMIZATION
Opposition-based learning was first introduced as a solution for machine learning; however, it is being extended to other artificial intelligence and soft computing fields including meta-heuristic optimization. It not only utilizes an estimate of a solution but also enters its counter-part information into the search process. The present work applies such an approach to Colliding Bodies Optimiz...
متن کامل