1563584
An empirical study of the correlation between code coverage and reliability estimation
Existing time-domain models for software reliability often result in an overestimation, of such reliability because they do not take the nature of testing techniques into account. Since every testing technique has a limit to its ability to reveal faults in a given system, as a technique approaches its saturation region fewer faults are discovered and reliability growth phenomena are predicted from the models. When the software is turned over to field operation, significant overestimates of reliability are observed. We present a technique to solve this problem by addressing both time and coverage measures for the prediction of software failures. Our technique uses coverage information collected during testing to extract only effective data from a given operational profile. Execution time between test cases which neither increase coverage nor cause a failure as reduced by a parameterized factor. Experiments using this technique were conducted on a program created in a simulation environment with simulated faults and on an industrial automatic flight control project which contained several natural faults. Results from both experiments indicate that overestimation of reliability is reduced significantly using our technique. This new approach not only helps reliability growth models make more accurate predictions, but also reveals the efficiency of a testing profile so that more effective testing techniques can be conducted.
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.
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.
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.
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.
1021507
Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning
Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly
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.
5758182
Cooperative Instruction Scheduling with Linear Scan Register Allocation
Linear scan register allocation is an attractive register allocation algorithm because of its simplicity and fast running time. However, it is generally felt that linear scan register allocation yields poorer code than allocation schemes based on graph coloring. In this paper, we propose a pre-pass instruction scheduling algorithm that improves on the code quality of linear scan allocators. Our implementation in the Trimaran compiler-simulator infrastructure shows that our scheduler can reduce the number of active live ranges that the linear scan allocator has to deal with. As a result, fewer spills are needed and the quality of the generated code is improved. Furthermore, compared to the default scheduling and graph-coloring allocator schemes found in the IMPACT and Elcor components of Trimaran, our implementation with our pre-pass scheduler and linear scan register allocator significantly reduced compilation times.
3492450
A framework for data prefetching using off-line training of Markovian predictors
An important technique for alleviating the memory bottleneck is data prefetching. Data prefetching solutions ranging from pure software approach by inserting prefetch instructions through program analysis to purely hardware mechanisms have been proposed. The degrees of success of those techniques are dependent on the nature of the applications. The need for innovative approach is rapidly growing with the introduction of applications such as object-oriented applications that show dynamically changing memory access behavior In this paper, we propose a novel framework for the use of data prefetchers that are trained off-line using smart learning algorithms to produce prediction models which captures hidden memory access patterns. Once built, those prediction models are loaded into a data prefetching unit in the CPU at the appropriate point during the runtime to drive the prefetching. On average by using table size of about 8KB size, we were able to achieve prediction accuracy of about 68% through our own proposed learning method and performance was boosted about 37% on average on the benchmarks we tested. Furthermore, we believe our proposed framework is amenable to other predictors and can be done as a phase of the profiling-optimizing-compiler.
4523050
Determination of Local Statistical Significance of Patterns in Markov Sequences with Application to Promoter Element Identification
High-level eukaryotic genomes present a particular challenge to the computational identification of transcription factor binding sites (TFBSs) because of their long noncoding regions and large numbers of repeat elements. This is evidenced by the noisy results generated by most current methods. In this paper, we present a p-value-based scoring scheme using probability generating functions to evaluate the statistical significance of potential TFBSs. Furthermore, we introduce the local genomic context into the model so that candidate sites are evaluated based both on their similarities to known binding sites and on their contrasts against their respective local genomic contexts. We demonstrate that our approach is advantageous in the prediction of myogenin and MEF2 binding sites in the human genome. We also apply LMM to large-scale human binding site sequences in situ and found that, compared to current popular methods, LMM analysis can reduce false positive errors by more than 50% without compromising sensitivity. This improvement will be of importance to any subsequent algorithm that aims to detect regulatory modules based on known PSSMs.