Dependency-Directed Localization of Software Bugs

Software bugs are violated specifications. Debugging is the process that culminates in repairing a program so that it satisfies its specification. An important part of debugging is localization, whereby the smallest region of the program that manifests the bug is found. The Debugging Assistant (DEBUSSI) localizes bugs by reasoning about logical dependencies. DEBUSSI manipulates the assumptions that underlie a bug manifestation, eventually localizing the bug to one particular assumption. At the same time, DEBUSSI acquires specification information, thereby extending its understanding of the buggy program. The techniques used for debugging fully implemented code are also appropriate for validating partial designs.

Dependency Directed Reasoning for Complex Program Understanding

Artificial Intelligence research involves the creation of extremely complex programs which must possess the capability to introspect, learn, and improve their expertise. Any truly intelligent program must be able to create procedures and to modify them as it gathers information from its experience. [Sussman, 1975] produced such a system for a 'mini-world'; but truly intelligent programs must be considerably more complex. A crucial stepping stone in AI research is the development of a system which can understand complex programs well enough to modify them. There is also a complexity barrier in the world of commercial software which is making the cost of software production and maintenance prohibitive. Here too a system which is capable of understanding complex programs is a necessary step. The Programmer's Apprentice Project [Rich and Shrobe, 76] is attempting to develop an interactive programming tool which will help expert programmers deal with the complexity involved in engineering a large software system. This report describes REASON, the deductive component of the programmer's apprentice. REASON is intended to help expert programmers in the process of evolutionary program design. REASON utilizes the engineering techniques of modelling, decomposition, and analysis by inspection to determine how modules interact to achieve the desired overall behavior of a program. REASON coordinates its various sources of knowledge by using a dependency-directed structure which records the justification for each deduction it makes. Once a program has been analyzed these justifications can be summarized into a teleological structure called a plan which helps the system understand the impact of a proposed program modification.

Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EMand the Minimum Spanning Tree algorithm to find the ML and MAP mixtureof trees for a variety of priors, including the Dirichlet and the MDL priors.

Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes. Experimental results demonstrate the excellent performance of the new model both in density estimation and in classification.

Suppliers' Associations in the Japanese Automobile Industry

This paper analyzes the structure and functions of suppliers' associations (kyoryokukai) in the automobile industry in Japan. The bilateral assembler-supplier relationship has received much attention recently as a source of Japanese industrial competitiveness. However, this paper argues that the hitherto neglected area of inter-supplier coordination in technology diffusion is at least as important as the bilateral assembler-supplier relationship in accounting for the overall performance of the Japanese automotive industry. On the basis of company visits and a largescale survey of first-tier suppliers conducted by the author, the paper analyzes the reasons why suppliers' associations were established, why they continue to exist today, and their effects on economic performance.

Stimulus Familiarity Determines Recognition Strategy for Novel 3-D Objects

We describe a psychophysical investigation of the effects of object complexity and familiarity on the variation of recognition time and recognition accuracy over different views of novel 3D objects. Our findings indicate that with practice the response times for different views become more uniform and the initially orderly dependency of the response time on the distance to a "good" view disappears. One possible interpretation of our results is in terms of a tradeoff between memory needed for storing specific-view representations of objects and time spent in recognizing the objects.

Truth Maintenance Systems for Problem Solving

The thesis developed here is that reasoning programs which take care to record the logical justifications for program beliefs can apply several powerful, but simple, domain-independent algorithms to (1) maintain the consistency of program beliefs, (2) realize substantial search efficiencies, and (3) automatically summarize explanations of program beliefs. These algorithms are the recorded justifications to maintain the consistency and well founded basis of the set of beliefs. The set of beliefs can be efficiently updated in an incremental manner when hypotheses are retracted and when new information is discovered. The recorded justifications also enable the pinpointing of exactly whose assumptions which support any particular belief. The ability to pinpoint the underlying assumptions is the basis for an extremely powerful domain-independent backtracking method. This method, called Dependency-Directed Backtracking, offers vastly improved performance over traditional backtracking algorithms.

Parallelism in Manipulator Dynamics

This paper addresses the problem of efficiently computing the motor torques required to drive a lower-pair kinematic chain (e.g., a typical manipulator arm in free motion, or a mechanical leg in the swing phase) given the desired trajectory; i.e., the Inverse Dynamics problem. It investigates the high degree of parallelism inherent in the computations, and presents two "mathematically exact" formulations especially suited to high-speed, highly parallel implementations using special-purpose hardware or VLSI devices. In principle, the formulations should permit the calculations to run at a speed bounded only by I/O. The first presented is a parallel version of the recent linear Newton-Euler recursive algorithm. The time cost is also linear in the number of joints, but the real-time coefficients are reduced by almost two orders of magnitude. The second formulation reports a new parallel algorithm which shows that it is possible to improve upon the linear time dependency. The real time required to perform the calculations increases only as the [log2] of the number of joints. Either formulation is susceptible to a systolic pipelined architecture in which complete sets of joint torques emerge at successive intervals of four floating-point operations. Hardware requirements necessary to support the algorithm are considered and found not to be excessive, and a VLSI implementation architecture is suggested. We indicate possible applications to incorporating dynamical considerations into trajectory planning, e.g. it may be possible to build an on-line trajectory optimizer.