Data quality: Some comments on the NASA software defect datasets

Background-Self-evidently empirical analyses rely upon the quality of their data. Likewise, replications rely upon accurate reporting and using the same rather than similar versions of datasets. In recent years, there has been much interest in using machine learners to classify software modules into defect-prone and not defect-prone categories. The publicly available NASA datasets have been extensively used as part of this research. Objective-This short note investigates the extent to which published analyses based on the NASA defect datasets are meaningful and comparable. Method-We analyze the five studies published in the IEEE Transactions on Software Engineering since 2007 that have utilized these datasets and compare the two versions of the datasets currently in use. Results-We find important differences between the two versions of the datasets, implausible values in one dataset and generally insufficient detail documented on dataset preprocessing. Conclusions-It is recommended that researchers 1) indicate the provenance of the datasets they use, 2) report any preprocessing in sufficient detail to enable meaningful replication, and 3) invest effort in understanding the data prior to applying machine learners

Integrate the GM(1,1) and Verhulst models to predict software stage effort

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Software effort prediction clearly plays a crucial role in software project management. In keeping with more dynamic approaches to software development, it is not sufficient to only predict the whole-project effort at an early stage. Rather, the project manager must also dynamically predict the effort of different stages or activities during the software development process. This can assist the project manager to reestimate effort and adjust the project plan, thus avoiding effort or schedule overruns. This paper presents a method for software physical time stage-effort prediction based on grey models GM(1,1) and Verhulst. This method establishes models dynamically according to particular types of stage-effort sequences, and can adapt to particular development methodologies automatically by using a novel grey feedback mechanism. We evaluate the proposed method with a large-scale real-world software engineering dataset, and compare it with the linear regression method and the Kalman filter method, revealing that accuracy has been improved by at least 28% and 50%, respectively. The results indicate that the method can be effective and has considerable potential. We believe that stage predictions could be a useful complement to whole-project effort prediction methods.National Natural Science Foundation of China and the Hi-Tech Research and Development Program of Chin

A general software defect-proneness prediction framework

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.BACKGROUND - Predicting defect-prone software components is an economically important activity and so has received a good deal of attention. However, making sense of the many, and sometimes seemingly inconsistent, results is difficult. OBJECTIVE - We propose and evaluate a general framework for software defect prediction that supports 1) unbiased and 2) comprehensive comparison between competing prediction systems. METHOD - The framework is comprised of 1) scheme evaluation and 2) defect prediction components. The scheme evaluation analyzes the prediction performance of competing learning schemes for given historical data sets. The defect predictor builds models according to the evaluated learning scheme and predicts software defects with new data according to the constructed model. In order to demonstrate the performance of the proposed framework, we use both simulation and publicly available software defect data sets. RESULTS - The results show that we should choose different learning schemes for different data sets (i.e., no scheme dominates), that small details in conducting how evaluations are conducted can completely reverse findings, and last, that our proposed framework is more effective and less prone to bias than previous approaches. CONCLUSIONS - Failure to properly or fully evaluate a learning scheme can be misleading; however, these problems may be overcome by our proposed framework.National Natural Science Foundation of Chin

Theoretical studies of 63Cu Knight shifts of the normal state of YBa2Cu3O7

The 63Cu Knight shifts and g factors for the normal state of YBa2Cu3O7 in tetragonal phase are theoretically studied in a uniform way from the high (fourth-) order perturbation formulas of these parameters for a 3d9 ion under tetragonally elongated octahedra. The calculations are quantitatively correlated with the local structure of the Cu2+(2) site in YBa2Cu3O7. The theoretical results show good agreement with the observed values, and the improvements are achieved by adopting fewer adjustable parameters as compared to the previous works. It is found that the significant anisotropy of the Knight shifts is mainly attributed to the anisotropy of the g factors due to the orbital interactions.Comment: 5 page

Wetting and bonding characteristics of selected liquid-metals with a high power diode laser treated alumina bioceramic

Changes in the wettability characteristics of an alumina bioceramic occasioned by high power diode laser (HPDL) surface treatment were apparent from the observed reduction in the contact angle. Such changes were due to the HPDL bringing about reductions the surface roughness, increases in the surface O2 content and increases in the polar component of the surface energy. Additionally, HPDL treatment of the alumina bioceramic surface was found to effect an improvement in the bonding characteristics by increasing the work of adhesion. An electronic approach was used to elucidate the bonding characteristics of the alumina bioceramic before and after HPDL treatment. It is postulated that HPDL induced changes to the alumina bioceramic produced a surface with a reduced bandgap energy which consequently increased the work of adhesion by increasing the electron transfer at the metal/oxide interface and thus the metal-oxide interactions. Furthermore, it is suggested that the increase in the work of adhesion of the alumina bioceramic after HPDL treatment was due to a correlation existing between the wettability and ionicity of the alumina bioceramic; for it is believed that the HPDL treated surface is less ionic in nature than the untreated surface and therefore exhibits better wettability characteristics

Quark deconfinement phase transition for improved quark mass density-dependent model

By using the finite temperature quantum field theory, we calculate the finite temperature effective potential and extend the improved quark mass density-dependent model to finite temperature. It is shown that this model can not only describe the saturation properties of nuclear matter, but also explain the quark deconfinement phase transition successfully. The critical temperature is given and the effect of $\omega$- meson is addressed.Comment: 18 pages, 7 figure

Investigations of the g factors and local structure for orthorhombic Cu^{2+}(1) site in fresh PrBa_{2}Cu_{3}O_{6+x} powders

The electron paramagnetic resonance (EPR) g factors g_x, g_y and g_z of the orthorhombic Cu^{2+}(1) site in fresh PrBa_{2}Cu_{3}O_{6+x} powders are theoretically investigated using the perturbation formulas of the g factors for a 3d^9 ion under orthorhombically elongated octahedra. The local orthorhombic distortion around the Cu^{2+}(1) site due to the Jahn-Teller effect is described by the orthorhombic field parameters from the superposition model. The [CuO6]^{10-} complex is found to experience an axial elongation of about 0.04 {\AA} along c axis and the relative bond length variation of about 0.09 {\AA} along a and b axes of the Jahn-Teller nature. The theoretical results of the g factors based on the above local structure are in reasonable agreement with the experimental data.Comment: 6 pages, 1 figur