Linearity and plan generation

In Ref. 3) a new linearity concept for proofs was introduced and its application to plan generation was proposed. An examination of these ideas revealed some troublespots which are discussed in this paper. Furthermore, three qualifications of the original linearity concept are presented which attempt to overcome these difficulties.     

Tractable plan existence does not imply tractable plan generation

We present a class, 3S, of planning instances such that the plan existence problem is tractable while plan generation is provably intractable for instances of this class. The class is defined by simple structural restrictions, all of them testable in polynomial‐time. Furthermore, we show that plan generation can be carried out in time bounded by a polynomial in the size of the input and the size of the generated solution. For this class, we propose a provably sound and complete incremental planner, i.e., a planner that can usually output an executable prefix of the final plan before it has generated the whole plan. This revised version was published online in June 2006 with corrections to the Cover Date.     

分布式作战计划辅助生成系统研究

文中针对高技术条件下空中战役特点给作战计划带来的复杂性、协作性和使其非结构化或半结构化越来越突出等问题，提出了一种基于CSCW技术和AI技术相结合的新的计划制定方式－分布式作战计划辅助生成系统（DOPGS），以适应未来高技术战争的需要，达到解决作战指挥中存在的计划手段落后、计划制定效率低等目的。     

State generation and automated class testing

The maturity of object‐oriented methods has led to the wide availability of container classes: classes that encapsulate classical data structures and algorithms. Container classes are included in the C++ and Java standard libraries, and in many proprietary libraries. The wide availability and use of these classes makes reliability important, and testing plays a central role in achieving that reliability. The large number of cases necessary for thorough testing of container classes makes automated testing essential. This paper presents a novel approach for automated testing of container classes based on combinatorial algorithms for state generation. The approach is illustrated with black‐box and white‐box test drivers for a class implemented with the red–black tree data structure, used widely in industry and, in particular, in the C++ Standard Template Library. The white‐box driver is based on a new algorithm for red–black tree generation. The drivers are evaluated experimentally, providing quantitative measures of their effectiveness in terms of block and path coverage. The results clearly show that the approach is affordable in terms of development cost and execution time, and effective with respect to coverage achieved. The results also provide insight into the relative advantages of black‐box and white‐box drivers, and into the difficult problem of infeasible paths. Copyright © 2000 John Wiley & Sons, Ltd.     

软件回归测试中的自动测试生成方法

软件回归测试一般使用现有的测试用例集进行测试,如何有效利用这些用例成为回归测试的关键。研究自动对现有测试用例集的扩展,包括自动改变测试用例的执行顺序、自动进行测试用例数目的增减等,提出基于现有测试用例随机生成新的测试用例集的方法。实验证明,该方法生成的测试文件能在原有环境下成功执行,能实现自动测试范围的扩大或缩小,以及测试时间的增加或减少,为软件回归测试提供了有用的自动化工具。     

Inductive program synthesis for therapy plan generation

Planning is investigated in an area where classical STRIPS-like approaches usually fail. The application domain is therapy (i.e. repair) for complex dynamic processes. The peculiarities of this domain are discussed in some detail for convincingly developing the characteristics of the inductive planning approach presented. Plans are intended to be run for process therapy. Thus, plans are programs. Because of the unavoidable vagueness and uncertainty of information about complex dynamic processes in the case of disturbance, therapy plan generation turns out to be inductive program synthesis. There is developed a graph-theoretically based approach to inductive therapy plan generation. This approach is investigated from the inductive inference perspective. Particular emphasis is put on consistent and incremental learning of therapy plans. Basic application scenarios are developed and compared to each other. The inductive inference approach is invoked to develop and investigate a couple of planning algorithms. The core versions of these algorithms are successfully implemented in Lisp and Prolog. The work has been partially supported by the German Federal Ministry for Research and Technology (BMFT) within the Joint Project (BMFT-Verbundprojekt)Wiscon onDevelopment of Methods for Intelligent Monitoring and Control under contract no. 413-4001-01 IW 204 B. Additionally, the second author’s work in learning theory received some support from the German Federal Ministry for Research and Technology (BMFT) within the Joint Project (BMFT-Verbundprojekt)Gosler onAlgorithmic Learning for Knowledge-Based Systems under contract no. 413-4001-01 IW 101 A. Oksana Arnold: She graduated from Leipzig University of Technology in 1990 with a Master’s Thesis on a rule interpreter for default reasoning. She received her PhD. in Computer Science in 1996 on therapy control for complex dynamic processes within a knowledge-based process supervision and control system. Recently, She works at the University of Leipzig within a research project on information and communication technologies for virtual enterprises. Her main scientific interest is both in knowledge-based process supervision and control, where she did a pioneering work on therapy plan generation, and in flexible information systems for new generation business applications. Klaus P. Jantke: He graduated from Humboldt University Berlin with a Master’s Thesis in 1975. He received his Ph. D. in Computer Science in 1979 and his Habilitation at Humboldt in 1984. He worked as the Head of a Research Laboratory in Theoretical Computer Science and as a Vice-Director of the Computing Center at Humboldt University. Since 1987, Dr. Jantke is full professor at Leipzig University of Technology. His main research interest is in algorithmic learning theory. Besides this, he contributes to case-based reasoning, where his special interest is in learning issues and in structural similarity, and to knowledge-based process supervision and control, especially to planning. Dr. Jantke is member of the ACM, the EATCS, and the GI.     

Perspectives on search strategies in automated test input generation

1 Introduction Automatically generating a limited number of high-quality inputs to reveal bugs,crashes,and hangs becomes a central problem in software testing research[1].The two most extensively studied approaches to automated test input generation are fuzzing[2]and dynamic symbolic execution[3,4](DSE).Both fuzzing and DSE can effectively generate structural test inputs for non-trivial programs without human aids,and are extensively studied in existing literatures[3-5].However,they are also considerably different.To further understand in what sense they are similar or different,and to understand the strengths and limitations of both techniques,we raise the following two questions:(Q1)How do existing fuzzing and DSE techniques model the input space and manage the search procedure?     

A constraint programming formulation for planning: from plan scheduling to plan generation

Planning research is recently concerned with the resolution of more realistic problems as evidenced in the many works and new extensions to the Planning Domain Definition Language (PDDL) to better approximate real problems. Researchers’ works to push planning algorithms and capture more complex domains share an essential ingredient, namely the incorporation of new types of constraints. Adding constraints seems to be the way of approximating real problems: these constraints represent the duration of tasks, temporal and resource constraints, deadlines, soft constraints, etc., i.e. features that have been traditionally associated to the area of scheduling. This desired expressiveness can be achieved by augmenting the planning reasoning capabilities, at the cost of slightly deviating the planning process from its traditional implicit purpose, that is finding the causal structure of the plan. However, the resolution of complex domains with a great variety of different constraints may involve as much planning effort as scheduling effort (and perhaps the latter being more prominent in many problems). For this reason, in this paper we present a general approach to model those problems under a constraint programming formulation which allows us to represent and handle a wide range of constraints. Our work is based on the original model of , an optimal temporal planner, and it extends the ’s formulation to deal with more expressive constraints. We will show that our general formulation can be used for planning and/or scheduling, from scheduling a given complete plan to generating the whole plan from scratch. However, our contribution is not a new planner but a constraint programming formulation for representing highly-constrained planning + scheduling problems.     

Optimal assembly plan generation: a simplifying approach

The main difficulty in the overall process of optimal assembly plan generation is the great number of different ways to assemble a product (typically thousands of solutions). This problem confines the application of most existing automated planning methods to products composed of only a limited number of components. The presented method of assembly plan generation belongs to the approach called “disassembly” and is founded on a new representation of the assembly process, with introduction of a new concept, the equivalence of binary trees. This representation allows to generate the minimal list of all non-redundant (really different) assembly plans. Plan generation is directed by assembly operation constraints and plan-level performance criteria. The method was tested for various assembly applications and compared to other generation approaches. Results show a great reduction in the combinatorial explosion of the number of plans. Therefore, this simplifying approach of assembly sequence modeling allows to handle more complex products with a large number of parts.     

Entropy-based automated wrapper generation for weblog data extraction

This paper proposes a fully automated information extraction methodology for weblogs. The methodology integrates a set of relevant approaches based on the use of web feeds and processing of HTML for the extraction of weblog properties. The approach includes a model for generating a wrapper that exploits web feeds for deriving a set of extraction rules automatically. Instead of performing a pairwise comparison between posts, the model matches the values of the web feeds against their corresponding HTML elements retrieved from multiple weblog posts. It adopts a probabilistic approach for deriving a set of rules and automating the process of wrapper generation. An evaluation of the model is conducted on a collection of weblogs reporting a prediction accuracy of 89 %. The results of this evaluation show that the proposed technique enables robust extraction of weblog properties and can be applied across the blogosphere.     

Receding horizon maneuver generation for automated highway driving

This paper focuses on the problem of decision-making and control in an autonomous driving application for highways. By considering the decision-making and control problem as an obstacle avoidance path planning problem, the paper proposes a novel approach to path planning, which exploits the structured environment of one-way roads. As such, the obstacle avoidance path planning problem is formulated as a convex optimization problem within a receding horizon control framework, as the minimization of the deviation from a desired velocity and lane, subject to a set of constraints introduced to avoid collision with surrounding vehicles, stay within the road boundaries, and abide the physical limitations of the vehicle dynamics. The ability of the proposed approach to generate appropriate traffic dependent maneuvers is demonstrated in simulations concerning traffic scenarios on a two-lane, one-way road with one and two surrounding vehicles.     

The automated generation of test cases for compilers

The testing of a compiler is a difficult process, since often a very high quality product is expected by the user, particularly with respect to the compiler's correctness. This paper seeks to review the main methods that have been proposed for the automation of part of this process, using the automatic generation of test cases and the automatic checking of the results. It then summarizes the advantages and disadvantages of the general approach and highlights several areas for further research.     

新的结构化测试数据自动生成方法

针对模拟退火算法,遗传算法应用于测试数据的自动生成的局限性,提出了一种基于GEMGA(基因表达散乱遗传算法)的结构化测试数据的自动生成的方法.讨论了路径的选择,提出了将控制流图与数据流图结合起来生成测试路径,通过TriType的分析结果说明了该方法的可行性.根据得到的测试路径将GEMGA应用到测试数据的自动生成,TdType的实验结果表明,GEMGA能生成更高质量的数据,并适用于较大规模的程序.     

Separating sequence overlap for automated test sequence generation

Finite state machines have been used to model a number of classes of system and there has thus been much interest in the automatic generation of test sequences from finite state machines. Many finite state machine based test techniques utilize sequences that check the final states of transitions, the most general such sequence being a separating sequence: an input sequence that distinguishes between two states of an FSM. When using such techniques the test sequence length can be reduced by utilizing overlap. This paper investigates overlap for separating sequences and shows how this can be incorporated into test sequence generation.     

Hierarchical GUI test case generation using automated planning

The widespread use of GUIs for interacting with software is leading to the construction of more and more complex GUIs. With the growing complexity come challenges in testing the correctness of a GUI and its underlying software. We present a new technique to automatically generate test cases for GUIs that exploits planning, a well-developed and used technique in artificial intelligence. Given a set of operators, an initial state, and a goal state, a planner produces a sequence of the operators that will transform the initial state to the goal state. Our test case generation technique enables efficient application of planning by first creating a hierarchical model of a GUI based on its structure. The GUI model consists of hierarchical planning operators representing the possible events in the GUI. The test designer defines the preconditions and effects of the hierarchical operators, which are input into a plan-generation system. The test designer also creates scenarios that represent typical initial and goal states for a GUI user. The planner then generates plans representing sequences of GUI interactions that a user might employ to reach the goal state from the initial state. We implemented our test case generation system, called Planning Assisted Tester for Graphical User Interface Systems (PATHS) and experimentally evaluated its practicality and effectiveness. We describe a prototype implementation of PATHS and report on the results of controlled experiments to generate test cases for Microsoft's WordPad     

A semantic approach for automated test oracle generation

This paper presents the design, implementation, and applications of a software testing tool, TAO, which allows users to specify and generate test cases and oracles in a declarative way. Extended from its previous grammar-based test generation tool, TAO provides a declarative notation for defining denotational semantics on each productive grammar rule, such that when a test case is generated, its expected semantics will be evaluated automatically as well, serving as its test oracle. TAO further provides a simple tagging mechanism to embed oracles into test cases for bridging the automation between test case generation and software testing. Two practical case studies are used to illustrate how automated oracle generation can be effectively integrated with grammar-based test generation in different testing scenarios: locating fault-inducing input patterns on Java applications; and Selenium-based automated web testing.     

Parametric-based path generation for automated vehicles at roundabouts

Urban environments are becoming more and more complex because several factors as consecutive crossroads or lanes changes. These scenarios demand specific infrastructures—i.e. roundabouts, for improving traffic flow compared with traditional intersections. A roundabout removes timeouts associated with traffic lights at crossroads and trajectory conflicts among drivers. However, it is a challenging scenario for both humans and automated vehicles. This work presents a path planning method for automated vehicle driving at roundabouts. The proposed system achieves a G¹ continuous path, minimizing curvature steps to increase smoothness, dividing the driving process in three stages: entrance maneuver, driving within the roundabout and exit maneuver. Parametric equations are generated to deal with automated roundabout driving. This approach allows a real time planning considering two-lane roundabouts, taking different exits. Tests in simulated environments and on our prototype platform—Cybercar—validate the system on real urban environments, showing the proper behavior of the system.