Infeasible Paths Research Articles

A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

In the simulation experiment of path planning of mobile robot based on firefly algorithm, it is found that the matching relationship between the number of fireflies and obstacles in the iterative process has significant conflict impacts on exploration ability and computational complexity of the algorithm. In order to solve the above problem, an optimal method of path planning based on firefly algorithm with self-adaptive population size is proposed. Firstly, the evaluation of degree of collision is established at the cost of avoiding collision. Based on the degree of collision of the population, two nonlinear functions are proposed to determine the population size. Then, individuals are added or deleted for the firefly population. Individuals are added randomly. The feasible solution and the infeasible solution are distinguished in firefly population, and delete the fireflies in the infeasible solution first when performing the eliminating operation. Finally, on the basis of the existing methods for dealing with infeasible paths, a coefficient that is adaptively adjusted according to the population size is introduced to control the degree to which the infeasible path approaches the feasible area. Compared with fixed population size firefly algorithm, the proposed algorithm has better performance in terms of solution stability, convergence speed and running time.

An Approach for Detecting Infeasible Paths Based on a SMT Solver

Software testing is an important means to ensure software quality. Testers need to ensure that every component of the software is tested correctly to achieve high coverage, such as path coverage, decision coverage, and branch coverage. An infeasible path is a path that cannot be traversed by any test cases. The existence of infeasible paths can waste test resources; therefore, detection of infeasible paths are necessary before path testing. This paper presents a static method for the detecting infeasible paths that is based on a satisfiability modulo theory (SMT) solver. First, the proposed method generates a sub-path set and converts the feasibility issues into inequalities. Second, a constraint solver is used to solve the inequalities and, then, the sub-paths are divided into two categories: infeasible sub-paths and undetermined sub-paths. The paths that were expanded from the latter will be tested again to determine their feasibility. Finally, the feasibility of all paths is detected. Most of the detection works are done on the sub-path set; therefore, our method provides an effective solution to the path-explosion problem. The experimental results showed that the proposed method can detect infeasible paths more accurately and effectively than most existing methods.

Detecting Interprocedural Infeasible Paths Based on Unsatisfiable Path Constraint Patterns

The static analysis plays an important role in many software engineering activities. However, the existence of infeasible paths, which causes lower program test coverage and several false positives in the results of the static analysis, has become one of the biggest challenges for the static analysis. In this paper, based on unsatisfiable path constraint patterns, we present a new approach to detect interprocedural infeasible paths. In our approach, we first discover nine unsatisfiable path constraint patterns by mining the common path constraint features of a large number of infeasible paths. Then, we detect the interprocedural infeasible paths; a detected path is deemed to be an interprocedural infeasible path if its simplified constraint conditions match one of the nine unsatisfiable path constraint patterns. To illustrate and verify the approach, an experimental study is performed on five open source C projects. The results show that compared with the existing approach, our approach requires less time on average and detects more interprocedural infeasible paths among the given paths.

Static Back-Stack Transition Analysis for Android

In Android, activity instances at an application’s run time are organized in a back stack, and users always interact with the top instance of the stack. Users’ interactions may trigger activity launchings, which change the state of the back stack. Therefore, back-stack transition analysis is closely related to activity transition analysis, which is fundamental for understanding Android behaviors. Existing activity transition analyses are not sensitive to back-stack-related configurations, which suppose all activities are launched with the default configuration, and thus model infeasible transitions and incorrect back-stack states when there are non-default configurations in an application. In this paper, we present a tool, BSSimulator, to perform the static back-stack transition analysis. The proposed analysis contains two types of graph constructions: the activity launching graph that represents activity transitions with extracted back-stack-related configurations, and the back-stack transition graph that simulates the changes of the back stack triggered by activity launchings and back events. The study conducted on 968 open source apps from F-droid indicates that non-default configurations are in common use. The evaluations on 35 commonly used apps validate the high-precision of the generated activity launching graphs, and indicate that the proposed back-stack transition analysis outperforms the state-of-the-art one by 29.8% in avoiding 3-length infeasible paths.

Automatic Classification of Program Paths Feasibility Using Active Learning

One of the challenging problems that faces the automated test data generation for path testing is the existence of infeasible paths, where no input data can be found to exercise them. Substantial time and effort may be wasted in trying to generate input data to exercise such paths. This paper proposes an active-learning approach to the automatic feasibility classification of program paths. This approach is based on the hypothesis that certain features of program behavior are stochastic processes that exhibit the Markov property, and that the resultant Markov models of individual program paths can be automatically clustered into effective predictors of path feasibility . To this end, the paper presents a technique that represents program paths as Markov models, and a clustering algorithm for Markov models that aggregates them into an effective path feasibility classifier. In this approach, the classifier is a map from program path statistics, namely, edge, branch, or definition-use profiles, to a label for the path, “feasible” or “infeasible”. The presented technique employs the bootstrapping active learning strategy, where the classifier is trained incrementally on a series of labeled instances, to extend its scope of training to be able to succeed in classifying new paths. The paper also presented the results of the experiments that were conducted to evaluate the effectiveness of the three paths feasibility classifiers built by using the proposed technique, and the bootstrapping technique.

Exact approaches for the directed network design problem with relays

We study the directed network design problem with relays (DNDPR) whose aim is to construct a minimum cost network that enables the communication of a given set of origin-destination pairs. Thereby, expensive signal regeneration devices need to be placed to cover communication distances exceeding a predefined threshold. Applications of the DNDPR arise in telecommunications and transportation. We propose two new integer programming formulations for the DNDPR. The first one is a flow-based formulation with a pseudo-polynomial number of variables and constraints and the second is a cut-based formulation with an exponential number of constraints. Fractional distance values are handled efficiently by augmenting both models with an exponentially-sized set of infeasible path constraints. We develop branch-and-cut algorithms and also consider valid inequalities to strengthen the obtained dual bounds and to speed up convergence. The results of our extensive computational study on diverse sets of benchmark instances show that our algorithms outperform the previous state-of-the-art method based on column generation.

Automated Test Case Generation Based on Differential Evolution With Relationship Matrix for iFogSim Toolkit

Fog computing plays an important role in industrial and information process. The programs in fog computing, such as iFogSim toolkit, usually contain some infeasible paths (paths that cannot be covered), which makes it impossible to compare algorithm in models that require covering all paths. In this paper, we proposed a mathematical model to build automated test case generation based on path coverage (ATCG-PC) in fog computing programs as a single-objective problem. Single objective helps to reduce the cost of evaluation functions, which is proportional to the number of test cases. When infeasible paths are contained in tested programs, algorithms can also be compared in this model. In this paper, classical differential evolution (DE) is used to solve the ATCG-PC. However, it is difficult for DE to use generated test cases covering remaining paths in the ATCG-PC of fog computing. Therefore, we proposed a test-case-path relationship matrix to empower DE (RP-DE). Experiment results show that RP-DE uses significantly less test cases and achieves higher path coverage rate than compared state-of-the-art algorithms.

Scalable and precise estimation and debugging of the worst-case execution time for analysis-friendly processors: a comeback of model checking

Estimating the Worst-Case Execution Time (WCET) of an application is an essential task in the context of developing real-time or safety-critical software, but it is also a complex and error-prone process. Conventional approaches require at least some manual inputs from the user, such as loop bounds and infeasible path information, which are hard to obtain and can lead to unsafe results if they are incorrect. This is aggravated by the lack of a comprehensive explanation of the WCET estimate, i.e., a specific trace showing how WCET was reached. It is therefore hard to spot incorrect inputs and hard to improve the worst-case timing of the application. Meanwhile, modern processors have reached a complexity that refutes analysis and puts more and more burden on the practitioner. In this article we show how all of these issues can be significantly mitigated or even solved, if we use processors that are amenable to WCET analysis. We define and identify such processors, and then we propose an automated tool set which estimates a precise WCET without unsafe manual inputs, and also reconstructs a maximum-detail view of the WCET path that can be examined in a debugger environment. Our approach is based on Model Checking, which however is known to scale badly with growing application size. We address this issue by shifting the analysis to source code level, where source code transformations can be applied that retain the timing behavior, but reduce the complexity. Our experiments show that fast and precise estimates can be achieved with Model Checking, that its scalability can even exceed current approaches, and that new opportunities arise in the context of "timing debugging".

Optimal priority and threshold assignment for fixed-priority preemption threshold scheduling

Fixed-priority preemption-threshold scheduling (FPTS) is a generalization of fixed-priority preemptive scheduling (FPPS) and fixed-priority non-preemptive scheduling (FPNS). Since FPPS and FPNS are incomparable in terms of potential schedulability, FPTS has the advantage that it can schedule any task set schedulable by FPPS or FPNS and some that are not schedulable by either. FPTS is based on the idea that each task is assigned a priority and a preemption threshold. While tasks are admitted into the system according to their priorities, they can only be preempted by tasks that have priority higher than the preemption threshold. This paper presents a new optimal priority and preemption threshold assignment (OPTA) algorithm for FPTS which in general outperforms the existing algorithms in terms of the size of the explored state-space and the total number of worst case response time calculations performed. The algorithm is based on back-tracking, i.e. it traverses the space of potential priorities and preemption thresholds, while pruning infeasible paths, and returns the first assignment deemed schedulable. We present the evaluation results where we compare the complexity of the new algorithm with the existing one. We show that the new algorithm significantly reduces the time needed to find a solution. Through a comparative evaluation, we show the improvements that can be achieved in terms of schedulability ratio by our OPTA compared to a deadline monotonic priority assignment.

Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning

In this study, an improved crossover operator is suggested, for solving path planning problems using genetic algorithms (GA) in static environment. GA has been widely applied in path optimization problem which consists in finding a valid and feasible path between two positions while avoiding obstacles and optimizing some criteria such as distance (length of the path), safety (the path must be as far as possible from the obstacles) ...etc. Several researches have provided new approaches used GA to produce an optimal path. Crossover operators existing in the literature can generate infeasible paths, most of these methods dont take into account the variable length chromosomes. The proposed crossover operator avoids premature convergence and offers feasible paths with better fitness value than its parents, thus the algorithm converges more rapidly. A new fitness function which takes into account the distance, the safety and the energy, is also suggested. In order to prove the validity of the proposed method, it is applied to many different environments and compared with three studies in the literature. The simulation results show that using GA with the improved crossover operators and the fitness function helps to find optimal solutions compared to other methods.

Path Planning of Mobile Robot Based on Hybrid Multi-Objective Bare Bones Particle Swarm Optimization With Differential Evolution

An improved path planning for mobile robots is proposed based on the hybrid multi-objective bare-bones particle swarm optimization with differential evolution. The mathematical model for robot path planning is firstly devised as a tri-objective optimization with three indices, i.e., the path length, the smoothness degree of a path, and the safety degree of a path. Then, a hybrid multi-objective bare bones particle swarm optimization is developed to generate feasible paths by combining infeasible paths blocked by obstacles with feasible paths via improved mutation strategies of differential evolution. In addition, a new Pareto domination with collision constraints is developed to select the personal best position of a particle according to the definition of the collision degree of a path. Simulation results confirm the effectiveness of our algorithm.

Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations

Deterministic global methods for flowsheet optimization have almost exclusively relied on an equation-oriented formulation where all model variables are controlled by the optimizer and all model equations are considered as equality constraints, which results in very large optimization problems. A possible alternative is a reduced-space formulation similar to the sequential modular infeasible path method employed in local flowsheet optimization. This approach exploits the structure of the model equations to achieve a reduction in problem size. The optimizer only operates on a small subset of the model variables and handles only few equality constraints, while the majority is hidden in externally defined functions from which function values and relaxations for the objective function and constraints can be queried. Tight relaxations and their subgradients for these external functions can be provided through the automatic propagation of McCormick relaxations. Three steam power cycles of increasing complexity are used as case studies to evaluate the different formulations. Unlike in local optimization or in previous sequential approaches relying on interval methods, the solution of the reduced-space formulation using McCormick relaxations enables dramatic reductions in computational time compared to the conventional equation-oriented formulation. Despite the simplicity of the implemented branch-and-bound solver that does not fully exploit the tight relaxations returned by the external functions but relies on further affine relaxation at a single point using the subgradients, in some cases it can solve the reduced-space formulation significantly faster without any range reduction than the state-of-the-art solver BARON can solve the equation-oriented formulation.

High-assurance timing analysis for a high-assurance real-time operating system

Worst-case execution time (WCET) analysis of real-time code needs to be performed on the executable binary code for soundness. Obtaining tight WCET bounds requires determination of loop bounds and elimination of infeasible paths. The binary code, however, lacks information necessary to determine these bounds. This information is usually provided through manual intervention, or preserved in the binary by a specially modified compiler. We propose an alternative approach, using an existing translation-validation framework, to enable high-assurance, automatic determination of loop bounds and infeasible paths. We show that this approach automatically determines all loop bounds and many (possibly all) infeasible paths in the seL4 microkernel, as well as in standard WCET benchmarks which are in the language subset of our C parser. We also design and validate an improvement to the seL4 implementation, which permits a key part of the kernel’s API to be available to users in a mixed-criticality setting.

Detection of Infeasible Paths in Software Testing using UML Application to Gold Vending Machine

Detection of Infeasible Paths in Software Testing using UML Application to Gold Vending Machine

Collision-Free Rendezvous Maneuvers for Formations of Unmanned Aerial Vehicles

This article discusses the rendezvous maneuver for a fleet of small fixed-wing Unmanned Aerial Vehicles (UAVs). Trajectories have to be generated on-line while avoiding collision with static and dynamic obstacles and minimizing rendezvous time. An approach based on Model Predictive Control (MPC) is investigated which assures that the dynamic constraints of the UAVs are satisfied at every time step. By introducing binary variables, a Mixed Integer Linear Programming (MILP) problem is formulated. Computation time is limited by incorporating the receding horizon technique. A shorter planning horizon strongly reduces computation time, but delays detection of obstacles which can lead to an infeasible path. The result is a robust path planning algorithm that satisfies the imposed constraints. However, further relaxation of the constraints and fine-tuning is necessary to limit complexity.

Cost and Effectiveness of Search-Based Techniques for Model-Based Testing: An Empirical Analysis

Model-based testing (MBT) seems to be gaining interest in industry and academia due to its provision of systematic, automated and comprehensive testing. The challenge in MBT is to generate optimal test data to execute test cases. Recently, researchers have successfully applied search-based techniques (SBTs) by automating the search for an optimal set of test data at reasonable cost compared to other more expensive techniques. In real complex systems, effectiveness and cost of SBTs for MBT in industrial context are little known. The objective of this study is to empirically evaluate the cost and the effectiveness of SBTs for MBT on industrial case studies. We applied a model-driven approach and SBTs to automatically generate executable feasible test cases. The results show that the model-driven approach generated high number of infeasible test cases with less time while genetic algorithm (GA) and simulating annealing (SA) outperformed significantly random search (RS) with high generation time. We concluded that local SBTs are more appropriate to generate test data when the type of the constraints is simple. Current work on analyzing the cost and effectiveness on SBTs for MBT indicates possible enhancement using the model-driven approach to detect the infeasible paths and SBTs to achieve optimal success rate.

Deriving Unbounded Reachability Proof of Linear Hybrid Automata during Bounded Checking Procedure

Reachability analysis of linear hybrid automata (LHA) is an important problem. Classical model checking (CMC) technique is not scalable and not guaranteed to terminate. On the other hand, bounded model checking (BMC) is more cost-effective to conduct but can not guarantee the safety beyond the bound. In this paper, we seek to bridge the gap between BMC and CMC for reachability analysis of LHA. During BMC of LHA, typical procedures can discover sets of unsatisfiable constraint cores, which can be mapped back to path segments in the graph structure of LHA. If every path connecting the initial and target location has to go through such infeasible path segment, the target location is entirely not reachable. Based on this characteristic, we propose a LTL model checking based approach to check whether the target location is blocked. To further optimize the performance, we propose an automata based solution to check the LTL specification incrementally and adopt an on-the-fly algorithm to check the accepting condition to avoid an explicit construction of product automata.

3Dana: A path planning algorithm for surface robotics

Autonomous navigation is a research topic that has received considerable attention in robotics. Generally, it is a two step process: (i) generate a global route to the goal and (ii) local motion of the robot along the route. The focus of this paper is on the first part of the process. Some common techniques used are based on heuristic search algorithms that obtain (sub)optimal paths by usually exploiting a rather simplistic terrain representation. Then, the paths generated hardly take into account relevant terrain features, which leads to potentially unsafe paths in realistic environments. This paper presents two contributions: a mathematical formulation for any DTM that can be used by heuristic search algorithms, and a path planning algorithm that generates candidate paths that are safer than the ones obtained by previous approaches. This algorithm, called 3Dana, considers different parameters to maximize the path quality: the maximum slope allowed by the robot and the heading changes during the path. These constraints allow discarding infeasible paths while minimizing the heading changes. To demonstrate the effectiveness of the algorithm proposed, we present results for different scenarios, which include an evaluation of the algorithm in real Mars DTMs.

Web Security: Detection of Cross Site Scripting in PHP Web Application using Genetic Algorithm

Cross site scripting (XSS) is one of the major threats to the web application security, where the research is still underway for an effective and useful way to analyse the source code of web application and removes this threat. XSS occurs by injecting the malicious scripts into web application and it can lead to significant violations at the site or for the user. Several solutions have been recommended for their detection. However, their results do not appear to be effective enough to resolve the issue. This paper recommended a methodology for the detection of XSS from the PHP web application using genetic algorithm (GA) and static analysis. The methodology enhances the earlier approaches of determining XSS vulnerability in the web application by eliminating the infeasible paths from the control flow graph (CFG). This aids in reducing the false positive rate in the outcomes. The results of the experiments indicated that our methodology is more effectual in detecting XSS vulnerability from the PHP web application compared to the earlier studies, in terms of the false positive rates and the concrete susceptible paths determined by GA Generator.

Extending the WCET Problem to Optimize for Runtime-Reconfigurable Processors

The correctness of a real-time system does not depend on the correctness of its calculations alone but also on the non-functional requirement of adhering to deadlines. Guaranteeing these deadlines by static timing analysis, however, is practically infeasible for current microarchitectures with out-of-order scheduling pipelines, several hardware threads, and multiple (shared) cache layers. Novel timing-analyzable features are required to sustain the strongly increasing demand for processing power in real-time systems. Recent advances in timing analysis have shown that runtime-reconfigurable instruction set processors are one way to escape the scarcity of analyzable processing power while preserving the flexibility of the system. When moving calculations from software to hardware by means of reconfigurable custom instructions (CIs)—additional to a considerable speedup—the overestimation of a task’s worst-case execution time (WCET) can be reduced. CIs typically implement functionality that corresponds to several hundred instructions on the central processing unit (CPU) pipeline. While analyzing instructions for worst-case latency may introduce pessimism, the latency of CIs—executed on the reconfigurable fabric—is precisely known. In this work, we introduce the problem of selecting reconfigurable CIs to optimize the WCET of an application. We model this problem as an extension to state-of-the-art integer linear programming (ILP)-based program path analysis. This way, we enable optimization based on accurate WCET estimates with integration of information about global program flow, for example, infeasible paths. We present an optimal solution with effective techniques to prune the search space and a greedy heuristic that performs a maximum number of steps linear in the number of partitions of reconfigurable area available. Finally, we show the effectiveness of optimizing the WCET on a reconfigurable processor by evaluating a complex multimedia application with multiple reconfigurable CIs for several hardware parameters.