679443
Effect of Test Set Minimization on Fault Detection Effectiveness
Size and code coverage are important attributes of a set of tests. When a program P is executed on elements of the test set T, we can observe the fault detecting capability of T for P. We can also observe the degree to which T induces code coverage on P according to some coverage criterion. We would like to know whether it is the size of T or the coverage of T on P which determines the fault detection effectiveness of T for P. To address this issue we ask the following question: While keeping coverage constant, what is the effect on fault detection of reducing the size of a test set? We report results from an empirical study using the block and all-uses criteria as the coverage measures.
39945640
Smart debugging software architectural design in SDL
Statistical data show that it is much less expensive to correct software bugs at the early design stage rather than the late stage of the development process when the final system has already been implemented and integrated together. The use of slicing and execution histories as an aid in software debugging is well established for programming languages like C and C++; however, it is rarely applied in the field of software design specification. We propose a solution by applying the source code level technologies to debugging software designs represented in a high-level specification and description language such as SDL. More specifically, we extend execution slice-based heuristics from source code-based debugging to the software design specification level. Suspicious locations in an SDL specification are prioritized by their likelihood of containing faults. Locations with a higher priority should be examined first rather than those with a lower priority as the former are more likely to contain the faults. A debugging tool, SmartD/sub DSL/, with user-friendly interfaces was developed to support our method. An illustration is provided to demonstrate the feasibility of using our method to effectively debug an architectural design.
31350744
Redesigning legacy systems into the object-oriented paradigm
Object-oriented/sup 1/ languages support many modern programming paradigms such as information hiding, inheritance, polymorphism, and dynamic binding. As a result, software systems implemented in OO languages are more reusable and reliable than those in non-OO. Many legacy software systems were created before OO programming became popular and needed to be redesigned and updated to OO programs. The process of abstracting OO designs from the procedural source code was often done manually or with limited assistance from program structural diagrams. Most reengineering focuses on the functionality of the original program, and the OO redesign often results in a completely new design based on the designers' understanding of the original program. Such an approach is not sufficient, for it not only takes time and effort for designers to become familiar with the original program, but the approach itself is also mistake-prone due to the human involvement. This paper presents a computer-aided semi-automatic method that abstracts OO designs from the original procedural source code. More specifically, it is a method for OO redesign based on program structural diagrams, visualization, and execution slice. We conducted a case study by applying this method to an inventory management software system. Results indicate that our method can effectively and efficiently abstract an appropriate OO design out of the original C code as well as re-generate part of the system in C++ code based on this new OO design.
30609851
An integrated solution for creating dependable software
Software testing and maintenance are the two most expensive phases of the software life cycle. Why, in general, are customers not satisfied with the quality of software? Studies have shown that approximately 20% of a software system is responsible for 80% of the faults and over the lifetime of an average software system as much as 70% of the cost is estimated to be dedicated to testing and maintenance. Clearly an appropriate methodology ought to be employed to address these high-leverage concerns. With the increased emphasis on software quality and the pressure to deliver software faster and cheaper such a methodology should encompass software design, architecture, development, testing, and maintenance as a whole and provide an integrated solution that leads to the creation of more dependable software. In addition, it is also crucial to have a set of effective tools with user-friendly interfaces available to software practitioners to apply this methodology in real-life contexts. We explain how to take advantage of sophisticated analysis of the dynamic behavior of the software being developed to help programmers, as well as testers, accomplish their assignments more effectively and efficiently.
13223545
Quantifying the closeness between program components and features
Abstract One of the most important steps towards effective software maintenance of a large complicated system is to understand how program features are spread over the entire system and their interactions with the program components. However, we must first be able to represent an abstract feature in terms of some concrete program components. In this paper, we use an execution slice-based technique to identify the basic blocks which are used to implement a program feature. Three metrics are then defined, based on this identification, to determine quantitatively , the disparity between a program component and a feature, the concentration of a feature in a program component, and the dedication of a program component to a feature. The computations of these metrics are automated by incorporating them in a tool ( χ Suds), which makes the use of our metrics immediately applicable in real-life contexts. We demonstrate the effectiveness of our technique by experimenting with a reliability and performance evaluator. Results of our study suggest that these metrics can provide an indication of the closeness between a feature and a program component which is very useful for software programmers and maintainers to better understand the system at hand.
206572113
Measuring distance between program features
We present a metric to determine the distance between the features of a software system. Such a measurement can elucidate how features of the system being examined are close to each other. We first use an execution slice-based technique to identify a set of code (basic blocks in our case) that is used to implement each feature. Then, depending on whether the execution frequency of each block is considered during the construction of such sets of code, a static as well as a dynamic distance is computed for each pair of features. These two types of distance differ in that the former computes the distance between two features only by how these features are implemented in the system, while the latter also takes into account how each feature is executed based on a user's operational profile. In other words, the static distance quantitatively gives the closeness of two features from the system implementation point of view, whereas the dynamic distance presents such closeness from the users' execution point of view. To illustrate the use of our metric, we report a case study on a Symbolic Hierarchical Automated Reliability and Performance Evaluator (SHARPE). The results of our study suggest that the distance metric discussed in this paper can provide a good measurement, in a quantitative way, of how close two program features are. Such information can also serve as a good start to understanding how a modification made to one feature is likely to affect other features.
6423442
Rule-based anomaly pattern detection for detecting disease outbreaks
This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied to our scenario, these traditional algorithms discover isolated outliers of particularly strange events, such as someone accidentally shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect anomalous patterns. These patterns are groups with specific characteristics whose recent pattern of illness is anomalous relative to historical patterns. We propose using a rule-based anomaly detection algorithm that characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for evaluation. The results indicate that our algorithm has significantly better detection times for common significance thresholds while having a slightly higher false positive rate.
8251892
Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning
We show how a conceptually simple search operator called Optimal Reinsertion can be applied to learning Bayesian Network structure from data. On each step we pick a node called the target. We delete all arcs entering or exiting the target. We then find, subject to some constraints, the globally optimal combination of in-arcs and out-arcs with which to reinsert it. The heart of the paper is a new algorithm called ORSearch which allows each optimal reinsertion step to be computed efficiently on large datasets. Our empirical results compare Optimal Reinsertion against a highly tuned implementation of multirestart hill climbing. The results typically show one to two orders of magnitude speed-up on a variety of datasets. They usually show better final results, both in terms of BDEU score and in modeling of future data drawn from the same distribution.
578051
Bayesian Biosurveillance of Disease Outbreaks
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.
2986922
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks
Early disease outbreak detection systems typically monitor health care data for irregularities by comparing the distribution of recent data against a baseline distribution. Determining the baseline is difficult due to the presence of different trends in health care data, such as trends caused by the day of week and by seasonal variations in temperature and weather. Creating the baseline distribution without taking these trends into account can lead to unacceptably high false positive counts and slow detection times. This paper replaces the baseline method of (Wong et al., 2002) with a Bayesian network which produces the baseline distribution by taking the joint distribution of the data and conditioning on attributes that are responsible for the trends. We show that our algorithm, called WSARE 3.0, is able to detect outbreaks in simulated data with almost the earliest possible detection time while keeping a low false positive count. We also include the results of running WSARE 3.0 on real Emergency Department data
15634785
The performance model of SilkRoad - a multithreaded DSM system for clusters
Distributed Shared Memory (DSM) is a highly desirable programming model for cluster based computing. Even though a number of software DSMs have been developed with their performance evaluated, few of them have a theoretical performance model. In this paper, we propose and analyze the performance model of the software DSM of SilkRoad,, a multithreaded runtime system for cluster computing. SilkRoad is built on the Cilk system with an extended memory consistency model which we call RC/sub dag/ consistency: Extending Cilk's theoretical performance model, we show that with the RC/sub dag/ consistent DSM, the expected execution.time T/sub P/ of a partially strict multithreaded computation on P processors is O((T/sub 1/(Z, L) + N)/P + /spl mu/HT/sub /spl infin//), where T/sub 1/(Z, L) is the total work of computation, T/sub /spl infin// is the critical path, N is the number of lock acquisitions, /spl mu/ is the service time of a cache miss, and H is the height of memory: Finally, we present experimental evidence that verify the performance model.