6398416
Analysis of the Linux Kernel Evolution Using Code Clone Coverage
Most studies of the evolution of software systems are based on the comparison of simple software metrics. In this paper, we present our preliminary investigation of the evolution of the Linux kernel using code-clone analysis and the code-clone coverage metrics. We examined 136 versions of the stable Linux kernel using a distributed extension of the code clone detection tool CCFinder. The result is shown as a heat map.
10307213
Very-Large Scale Code Clone Analysis and Visualization of Open Source Programs Using Distributed CCFinder: D-CCFinder
The increasing performance-price ratio of computer hardware makes possible to explore a distributed approach at code clone analysis. This paper presents D-CCFinder, a distributed approach at large-scale code clone analysis. D-CCFinder has been implemented with 80 PC workstations in our student laboratory, and a vast collection of open source software with about 400 million lines in total has been analyzed with it in about 2 days. The result has been visualized as a scatter plot, which showed the presence of frequently used code as easy recognizable patterns. Also, D-CCFinder has been used to analyze a single software system against the whole collection in order to explore the presence of code imported from open source software.
39858026
Automated Abduction
In this article, I review Peirce's abduction in the context of Artificial Intelligence. First, I connect abduction from first-order theories with nonmonotonic reasoning. In particular, I consider relationships between abduction, default logic, and circumscription. Then, based on a first-order characterization of abduction, I show a design of abductive procedures that utilize automated deduction. With abductive procedures, proof procedures for nonmonotonic reasoning are also obtained from the relationship between abduction and nonmonotonic reasoning.
12093499
Speculative computation by abduction under incomplete communication environments
We present a method of problem solving in multi-agent systems when communication between agents is not guaranteed. To solve the problem of incomplete communication, we propose a method using abduction. The idea is as follows. When communication is delayed or failed, then we use a default hypothesis as a tentative answer and continue computation. When some response is obtained, we check consistency of the response and the current computation. If the response is consistent, then we continue the current computation, or else if the response is inconsistent, we seek an alternative computation. This way of computation is called speculative computation, since computation using a tentative answer would lead to a significant advantage if it succeeds. In this paper, we restrict our attention to a master-slave multi-agent system and propose an implementation of speculative computation and show that abduction plays an important role in speculative computation.
36085581
An Abductive Procedure for the CMS/ATMS
This paper concerns procedural semantics for a variety of ATMSs. Reiter & de Kleer view an ATMS as a kind of abduction in which the best explanation of a formula is defined as a minimal conjunction of hypotheses that explain the formula. However, they do not give any algorithm to compute such minimal explanations of a formula in their CMS that is a generalization of de Kleer's basic ATMS. In this paper, we use the notion of characteristic clauses to make precise definitions of the CMS and the ATMS and to produce a sound and complete abductive procedure based on an extension of linear resolution. By means of this abductive procedure, we give the CMS algorithms for computing minimal explanations in the interpreted approach and for updating them in the compiled approach. We then present algorithms for generating and updating labels of nodes in an extended ATMS that accepts non-Horn justifications and literal assumptions. Finally, how a variation of the abductive procedure can be used to answer queries for circumscription of ground theories is presented.
11399248
Linear Resolution for Consequence Finding
Abstract In this paper, we re-evaluate the consequence finding problem within first-order logic. Firstly, consequence finding is generalized to the problem in which only interesting clauses having a certain property (called characteristic clauses) should be found. The use of characteristic clauses enables characterization of various reasoning problems of interest to AI, including abduction, nonmonotonic reasoning, prime implicates and truth maintenance systems. Secondly, an extension of the Model Elimination theorem proving procedure (SOL-resolution) is presented, providing an effective mechanism complete for finding the characteristic clauses. An important feature of SOL-resolution is that it constructs such a subset of consequences directly without testing each generated clause for the required property. We also discuss efficient but incomplete variations of SOL-resolution and their properties, which address finding the most specific and the least specific abductive explanations.