Lanubile, F., Lonigro, A., and Visaggio, G. 1995. Comparing models for identifying fault-prone software components. Proceedings of the 7th International Conference on Software Engineering and Knowledge Engineering. Washington, D.C., 312--319. Home/Search Document Details and Download
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Test Case Prioritization: A Family of Empirical Studies - Elbaum, Malishevsky.. (2001) (7 citations) (Correct)
....indexes to schedule test cases. In addition, there are other factors, such as test exposure capability, program domain, particular processes, and technique scalability, that may have not been relevant in the earlier studies, but could have had a significant impact on the fault prediction procedure [22] and in the FI based prioritization techniques effectiveness. These limitations might be contributing to some of the differences that we observe across the case studies. 15 15 Repeatability problems such as this, where different studies yield different results, are not unique to testing. For ....
....techniques effectiveness. These limitations might be contributing to some of the differences that we observe across the case studies. 15 15 Repeatability problems such as this, where different studies yield different results, are not unique to testing. For example, Lanubile et al. [22] report that even successful fault proneness prediction models might not work on every data set, and that there is a need to take into consideration the context in which they are used. 31 This said, FI based techniques were observed to result in improved APFD values in our controlled ....
F. Lanubile, A. Lonigro, and G. Visaggio. Comparing models for identifying fault-prone software components. In Proc. of the 7th Int'l. Conf. Softw. Eng. and Knowledge Eng., pages 312--319, June 1995.
Incorporating Varying Test Costs and Fault Severities into Test .. - Elbaum, al. (2001) (5 citations) (Correct)
....of a fault occurring in function i, and our sum is across the functions covered by test case j. Additional Fault Index Prioritization (fn fi addtl) Certain functions are more likely to contain faults than others, and this fault proneness can be associated with measurable software attributes [1, 5, 7]. Additional fault index prioritization takes advantage of this association by prioritizing test cases based on their history of executing fault prone functions. To represent fault proneness, we use fault indexes based on principal component analysis [3, 9] Given these fault indexes, additional ....
F. Lanubile, A. Lonigro, and G. Visaggio. Comparing models for identifying fault-prone software components. In Proc. 7th Int'l. Conf. on Softw. Engr. and Knowledge Engr., pages 12-- 19, June 1995.
Evaluating Empirical Models for the Detection of High-Risk.. - Lanubile, Visaggio (1995) Self-citation (Lanubile Visaggio) (Correct)
....three classification paradigms: principal component analysis, discriminant analysis, logistic regression, logical classification models, layered neural networks, and holographic networks. A detailed description of our implementation choices in building the classification models can be found in [LLV95]. 2. Data Description Raw data were obtained from 27 projects performed in a software engineering course at the University of Bari, by different three student teams over a period of 4 10 months. The systems, business applications developed from a same specification, range in size from 1100 to ....
F. Lanubile, A. Lonigro, and G. Visaggio, "Comparing models for identifying faultprone software components", in Proceedings of the 7th International Conference on Software Engineering and Knowledge Engineering, Rockville, Maryland, USA, June 1995, pp.312-319.
Fault-Threshold Prediction with Linear Programming.. - Maurizio Pighin Department (2003) (Correct)
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Lanubile, F., Lonigro, A., and Visaggio, G. 1995. Comparing models for identifying fault-prone software components. Proceedings of the 7th International Conference on Software Engineering and Knowledge Engineering. Washington, D.C., 312--319.
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