User-friendly formal specification languages - conclusions drawn from industrial experience on model checking

2016 ◽  
Author(s):  
Antti Pakonen ◽  
Cheng Pang ◽  
Igor Buzhinsky ◽  
Valeriy Vyatkin
Keyword(s):  
Model Checking ◽  
Formal Specification ◽  
Specification Languages ◽  
Industrial Experience ◽  
User Friendly

An Empirical Investigation of Requirements Specification Languages

2011 ◽  
pp. 125-147
Author(s):  
Erik Kamsties ◽  
Antje von Knethen ◽  
Jan Philipps
Keyword(s):  
Empirical Study ◽  
Formal Specification ◽  
Side Effect ◽  
Quantitative Data ◽  
Empirical Investigation ◽  
Requirements Specification ◽  
Specification Languages ◽  
Correct Interpretation ◽  
Research Questions ◽  
Detection Of Defects

A well-known side-effect of applying requirements specification languages is that the formalization of informal requirements leads to the detection of defects such as omissions, conflicts, and ambiguities. However, there is little quantitative data available on this effect. This chapter presents an empirical study of requirements specification languages, in which two research questions are addressed: Which types of defects are detected by a requirements engineer during formalization? Which types of defects go undetected and what happens to those types in a formal specification? The results suggest looking explicitly for ambiguities during formalization, because they are less frequently detected than other types of defects. If they are detected, they require immediate clarification by the requirements author. The majority of ambiguities tend to become disambiguated unconsciously, that is, the correct interpretation was chosen, but without recurring to the requirements author. This is a serious problem, because implicit assumptions are known to be dangerous.

A Generic Approach on How to Formally Specify and Model Check Path Finding Algorithms: Dijkstra, A* and LPA*

2020 ◽  
Vol 30 (10) ◽  
pp. 1481-1523
Author(s):  
Kazuhiro Ogata
Keyword(s):  
Model Checking ◽  
Shortest Path ◽  
Formal Specification ◽  
Model Check ◽  
Shortest Paths ◽  
Path Finding ◽  
Property A ◽  
Goal Node ◽  
The Cost ◽  
Start Node

The paper describes how to formally specify three path finding algorithms in Maude, a rewriting logic-based programming/specification language, and how to model check if they enjoy desired properties with the Maude LTL model checker. The three algorithms are Dijkstra Shortest Path Finding Algorithm (DA), A* Algorithm and LPA* Algorithm. One desired property is that the algorithms always find the shortest path. To this end, we use a path finding algorithm (BFS) based on breadth-first search. BFS finds all paths from a start node to a goal node and the set of all shortest paths is extracted. We check if the path found by each algorithm is included in the set of all shortest paths for the property. A* is an extension of DA in that for each node [Formula: see text] an estimation [Formula: see text] of the distance to the goal node from [Formula: see text] is used and LPA* is an incremental version of A*. It is known that if [Formula: see text] is admissible, A* always finds the shortest path. We have found a possible relaxed sufficient condition. The relaxed condition is that there exists the shortest path such that for each node [Formula: see text] except for the start node on the path [Formula: see text] plus the cost to [Formula: see text] from the start node is less than the cost of any non-shortest path to the goal from the start. We informally justify the relaxed condition. For LPA*, if the relaxed condition holds in each updated version of a graph concerned including the initial graph, the shortest path is constructed. Based on the three case studies for DA, A* and LPA*, we summarize the formal specification and model checking techniques used as a generic approach to formal specification and model checking of path finding algorithms.

scholarly journals Logic-based specification and verification of homogeneous dynamic multi-agent systems

2020 ◽  
Vol 34 (2) ◽  
Author(s):  
Riccardo De Masellis ◽  
Valentin Goranko
Keyword(s):  
Normal Form ◽  
Model Checking ◽  
Formal Specification ◽  
Formal Semantics ◽  
Fixed Number ◽  
State Transitions ◽  
Multi Agent Systems ◽  
Worst Case ◽  
Worst Case Complexity ◽  
Multi Agent

Abstract We develop a logic-based framework for formal specification and algorithmic verification of homogeneous and dynamic concurrent multi-agent transition systems. Homogeneity means that all agents have the same available actions at any given state and the actions have the same effects regardless of which agents perform them. The state transitions are therefore determined only by the vector of numbers of agents performing each action and are specified symbolically, by means of conditions on these numbers definable in Presburger arithmetic. The agents are divided into controllable (by the system supervisor/controller) and uncontrollable, representing the environment or adversary. Dynamicity means that the numbers of controllable and uncontrollable agents may vary throughout the system evolution, possibly at every transition. As a language for formal specification we use a suitably extended version of Alternating-time Temporal Logic, where one can specify properties of the type “a coalition of (at least) n controllable agents can ensure against (at most) m uncontrollable agents that any possible evolution of the system satisfies a given objective $$\gamma$$ γ ″, where $$\gamma$$ γ is specified again as a formula of that language and each of n and m is either a fixed number or a variable that can be quantified over. We provide formal semantics to our logic $${\mathcal {L}}_{\textsc {hdmas}}$$ L H D M A S and define normal form of its formulae. We then prove that every formula in $${\mathcal {L}}_{\textsc {hdmas}}$$ L H D M A S is equivalent in the finite to one in a normal form and develop an algorithm for global model checking of formulae in normal form in finite HDMAS models, which invokes model checking truth of Presburger formulae. We establish worst case complexity estimates for the model checking algorithm and illustrate it on a running example.

Efficient Bounded Model Checking for LTL

2013 ◽  
Vol 380-384 ◽  
pp. 1239-1242
Author(s):  
Rui Wang ◽  
Xian Jin Fu
Keyword(s):  
Model Checking ◽  
Efficient Method ◽  
Bounded Model Checking ◽  
Specification Languages ◽  
Model Checker ◽  
System Designs

Bounded Model Checking is an efficient method of finding bugs in system designs. LTL is one of the most frequently used specification languages in model checking. In this paper, We present an linearization encoding for LTL bounded model checking. We use the incremental SAT technology to solve the BMC problem. We implement the new encoding in NuSMV model checker.

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