16987100
Measuring Prime Program Complexity
Abstract This paper uses the prime program decomposition of a program as the basis for a measure that closely correlates with our intuitive notion of program complexity. This measure is based upon the information theory ideas of randomness and entropy such that results about structured programming, data abstractions, and other programming paradigms can be stated in quantitative terms, and empirical means can be used to validate the assumptions used to develop the model. As a graph-based model, it can be applied to several graphical examples as extensions not otherwise available to source-code based models. This paper introduces the measure, derives several properties for it, and gives some simple examples to demonstrate that the measure is a plausible approximation of our notions concerning structured programming.
19924236
Test-Execution-Based Reliability Measurement and Modeling for Large Commercial Software
The paper studies practical reliability measurement and modeling for large commercial software systems based on test execution data collected during system testing. The application environment and the goals of reliability assessment were analyzed to identify appropriate measurement data. Various reliability growth models were used on failure data normalized by test case executions to track testing progress and provide reliability assessment. Practical problems in data collection, reliability measurement and modeling, and modeling result analysis were also examined. The results demonstrated the feasibility of reliability measurement in a large commercial software development environment and provided a practical comparison of various reliability measurements and models under such an environment. >
39882874
A comparison of measurement and defect characteristics of new and legacy software systems
Abstract This paper compares the quality characteristics of a large legacy software system and a new one. Defect fixes applied to specific modules during testing are used as the direct quality metric. Indirect quality indicators used in this paper include software metrics of various product and process attributes, including design, size, change, and complexity. We analyze and compare the measurement results by examining their individual distributions, the correlations between defects and quality indicators, and tree-based models linking defects to quality indicators. In both these systems, most of the defects are found to be concentrated on relatively few high-defect modules, which points to the need for appropriate risk identification techniques so that defect removal effort can be focused on those high-defect modules for effective quality improvement. In addition, defects in the legacy system are more closely related to change and data complexity metrics; while defects in the new system are more closely related to various design metrics. These results demonstrate different measurement characteristics for these two types of software systems, and suggest that different quality analysis and improvement methods may be more appropriate and effective for different kinds of software systems.
29205217
Improving the Reliability of Object-Oriented Software through Object-Level Behavioral Simulation
In this paper we propose an Object-level Behavior Simulation Model (OBSM) that simulates the behavior of each object according to the message pattern exhibited by behavioral design pattern to verify it for its correctness. The model evaluates the existing reliability of the object-oriented software and allows developer to improve it during early phases of the development.
30175148
Fingerprint enhancement with dyadic scale-space
Fingerprint enhancement is a critical step in fingerprint identification. Most of the existing enhancement uses a set of contextual fillers to enhance fingerprint. The main drawback of these methods is these contextual filters based on the local information of the fingerprint, such as ridge width, orientation, curvature et al. These information are unreliable in the areas corrupted by the noise. This paper introduces the scale space theory in the computer vision to enhance the fingerprint. In the enhancement process, decompose fingerprint into a series of images and organize the images by finer to coarser scheme. Thus a globe and integrate interpretation is available and it enable us to get rid of the influence of noise to the largest extent. Experiments show our algorithm is fast and has excellent performance.
36734863
Knowledge based fingerprint image enhancement
Fingerprint enhancement is a critical step in fingerprint identification. Most of the existing enhancement algorithms are based on local ridge direction. The main drawback of these methods lies in the fact that false estimate of local ridge direction will lead to poor enhancement. But the estimate of local ridge directions is unreliable in the areas corrupted by noise where enhancement is most needed. In this paper, we proposed a rule-based method to do fingerprint enhancement. We introduced human knowledge about fingerprints into the enhancement process in the form of rules and simulate what an expert will do to enhance a fingerprint image. In our method, the skeleton image is used to give ridge connection information for the enhancement of the binary image. Experiments show our algorithm is fast and has excellent performance.
10434536
Identifying Linear Causal Effects
This paper concerns the assessment of linear cause-effect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.
4621823
On the Testable Implications of Causal Models with Hidden Variables
The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the generated data. In the presence of unmeasured variables, causal models may impose two types of constraints: conditional independencies, as read through the d-separation criterion, and functional constraints, for which no general criterion is available. This paper offers a systematic way of identifying functional constraints and, thus, facilitates the task of testing causal models as well as inferring such models from data.
16586772
A new state-space approach for super-resolution image sequence reconstruction
Super-resolution imaging is to overcome the inherent limitations of image acquisition to create high-resolution images from their low-resolution counterparts. In this paper, a novel state-space approach is proposed to incorporate the temporal correlations among the low-resolution observations into the framework of the Kalman filtering. The proposed approach exploits both the temporal correlations information among the high-resolution images and the temporal correlations information among the low-resolution images to improve the quality of the reconstructed high-resolution sequence. Experimental results show that the proposed framework is superior to bi-linear interpolation, bi-cubic spline interpolation and the conventional Kalman filter approach, due to the consideration of the temporal correlations among the low-resolution images.