Spectrum-based Fault Localization has emerged as a cost effective method to locate faulty code in software during the debugging process. Recent studies have shown that spectra (execution profiles) cloning for fail test cases can effectively improve the performance of certain spectrum-based Fault Localization ranking metrics. However, the amount of cloning required to optimize the performance varies from one program to another. This is because the structure and fault content of each program is unique. Furthermore, both insufficient cloning and excessive cloning may result in performance deterioration. In this paper, we propose an incremental spectra cloning algorithm that will clone the spectra of fail test cases up to an amount that optimizes the fault localization performance of the ranking metrics used for the software debugged. Experiments conducted on faulty versions of real life software have shown that the proposed algorithm could optimize the cost effectiveness of spectrum-based fault localization by reducing the number lines examined to locate the faulty code. 1650 Patrick Daniel et al.

Spectrum-based Fault Localization (SBFL) has been widely studied as a debugging technique to reduce time and effort in locating faulty code in software. In SBFL, execution profiles (spectra) of pass and fail test cases are analyzed with SBFL metric to rank software code according to their likeliness to be faulty. However, there are significantly more pass test cases than fail test cases in typical test suites for faulty programs. The domination of pass test cases creates imbalance test suites that have a negative impact on the performance of SBFL. This is attributed to the fact that the execution profiles of fail test cases provide the essential information on the location of faulty code. In this paper, we propose to clone the spectra of fail test cases beyond balanced test suite to improve the performance of SBFL. Our empirical study shows that the proposed cloning method significantly improves the performance of commonly used SBFL metrics. Furthermore, we attempt to identify the amount of cloning required to achieve the optimal performance for the proposed cloning method.

Spectrum-based Fault Localization (SBFL) is a popular fault localization technique that ranks statements in a program according to their suspiciousness to be faulty based on the statement execution records (spectra) of pass and fail test cases. Many SBFL metrics have been proposed with varying accuracies in ranking of faulty statement. In this paper we proposed a new SBFL metric based on a pair scoring approach. We evaluated the performance of the proposed metric and compare it with other existing SBFL metrics. Despite its simplicity, we found the proposed metric outperformed majority of the existing SBFL metrics. 

Spectrum-based Fault Localization (SBFL) is a well-known debugging technique that locates fault in program code by utilizing execution profiles (spectra) of pass and fail test cases. Hence, the performance of SBFL depends on the test cases executed and the test results. In the most extreme scenarios, the debugging process may have to be conducted with only one fail test case, one pass test case or no pass test case. These scenarios might occur due to extremely high or extremely low failure rates or when software testers decide to stop running more test cases due to time and resource constraints. However, limited test case execution profiles may reduce the accuracy of SBFL metrics. In view of this, we evaluate the performance of SBFL metrics in these extreme scenarios to identify the best performing SBFL metric for each of these scenarios. From the experiment results, we have further discovered the convergence in performance for SBFL metrics under these extreme scenarios.

Spectrum-based Fault Localization (SBFL) has been proven to be an effective technique to locate faulty statement in program code. SBFL metrics exploit the records of statement execution (spectra) by pass and fail test cases for each statement to rank its likeliness to be faulty. However, in some cases, these spectra contain duplicated and ambiguous information or noise which may deteriorate the performance of SBFL metrics. We propose six noise reduction schemes to eliminate test cases which provide duplicated and ambiguous information and evaluate the resulting performance improvements in SBFL metrics. Based on our findings, we further provide a guide for SBFL practitioners to select the best performing noise reduction scheme for the SBFL metrics that they use.

Spectrum-based Fault Localization (SBFL) is an emerging debugging technique that assists software developers to locate faulty code in software. By utilizing code execution information (spectra), SBFL metrics rank lines of codes in software according to their likeliness to be faulty. However, recent studies showed that contradicting, duplicated or noisy spectra may deteriorate the ranking accuracy of SBFL metrics. In this paper, we propose and develop a novel SBFL tool with test case preprocessor to filter out test cases with contradicting, duplicated or other noisy spectra. Case studies conducted on real life faulty programs show that the proposed SBFL tool with test case preprocessor has successfully improved the performance of SBFL metrics in majority of the cases studied.

Fault-based testing has been proven to be a cost effective testing technique for software logics and rules expressed in Boolean expressions. It can guarantee the elimination of common faults without exhaustive testing. However, average software testing practitioners may not have in-depth knowledge on Boolean algebra and complex logic derivations required to apply existing fault-based testing techniques. In this paper, a dynamic fault visualization tool has been proposed. This tool allows its user to visualize fault-based testing and prioritize test inputs with a simple greedy method. The performance evaluation of this tool has been done on Boolean expressions extracted from a real life aviation tool. The results show that it can achieve significant performance improvements compared to ordinary sequential order test execution and existing static technique. The proposed visualization tool could also identify possible faults to guide the debugging process.

An Ozark hellbender (Cryptobranchus alleganiensis bishopi) withmultiple, large, warty skin lesions was collected in the Spring River, Fulton County, Arkansas, in 1994. The specimen was a female, 560 mm in total length, and had a mass of 1,947 g. Tissues were formalin-fixed, and three lesions were processed for histopathology. The normal skin at the tumor margins had a stratified squamous epidermis overlying a loose, well-vascularized, heavily pigmented dermis. Poison glands and mucous glands extended from the epidermis into the dermis. The lesions, in contrast, were masses of epidermal cells up to 100 times thicker than the normal epidermis. They consisted of long, thick, branching epidermal pegs separated by thin fibrovascular papillae. The base of the lesions and all pegs had sharp boundaries bordered by a basement membrane, ruling out invasion. Tumor cells were differentiated into scattered glandular structures suggestive of dermal glands. Cells within the pegs were poorly organized. Nuclei usually contained basophilic granules. Mitotic figures were numerous. By electron microscopy, the cells appeared to be pulling apart except where held together by desmosomes. The sum of the above observations is consistent with a pathological diagnosis of epidermal papilloma.

Abstract

We conducteda tag and release study of the Ozarkhellbenderalong a 26 km stretch of the Spring River frommid-July through mid-November,1991, to determinecurrent population levels.Salamanders were collectedby hand withthe aid of scuba diving equipment. Thirteen visits(36 divehrs.) to 10 selected access sites yielded 20 animals. Compared to previously published data of the early 1980's which indicatedlarge, striving populations of C. bishopi (in some cases, > 300 individuals) in the Spring River, our study found perilously low numbers of salamanders. This drastic decline may be attributed to overcollection of specimens for scientific or other purposes and habitat alterationrelated to recreational activities.Other contributing factors for this decline could be the inadventent killing of animals during humanactivity (seining, swimming, canoeing, and fishing), the elimination of riparian habitats leading to an increase in the silt burden, and water pollution associated withhuman occupation and development along theriver.

We conducteda tag and release study of the Ozarkhellbenderalong a 26 km stretch of the Spring River frommid-July through mid-November,1991, to determinecurrent population levels.Salamanders were collectedby hand withthe aid of scuba diving equipment. Thirteen visits(36 divehrs.) to 10 selected access sites yielded 20 animals. Compared to previously published data of the early 1980's which indicatedlarge, striving populations of C. bishopi (in some cases, > 300 individuals) in the Spring River, our study found perilously low numbers of salamanders. This drastic decline may be attributed to overcollection of specimens for scientific or other purposes and habitat alterationrelated to recreational activities.Other contributing factors for this decline could be the inadventent killing of animals during humanactivity (seining, swimming, canoeing, and fishing), the elimination of riparian habitats leading to an increase in the silt burden, and water pollution associated withhuman occupation and development along theriver.