Statement spectrum with two dimensional eigenvalues for intelligent software fault localization

The existing Software Fault Localization Frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of fault localization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software fault localization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as fault localization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software fault localization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth.

Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization

The effectiveness analysis of risk evaluation formulas has become a significant research area in spectrum-based fault localization (SBFL). The risk evaluation formula is designed and widely used to evaluate the likelihood of a program spectrum to be faulty. There are numerous empirical and theoretical studies to investigate and compare the performance between sixty risk evaluation formulas. According to previous research, these sixty risk evaluation formulas together form a partially ordered set. Among them, nine formulas are maximal. These nine formulas can further be grouped into five maximal risk evaluation formula groups so that formulas in the same group have the same performance. Moreover, previous research showed that we cannot theoretically compare formulas across these five maximal formula groups. However, experimental data “suggests” that a maximal formula in one group could outperform another one (from a different group) more frequently, though not always. This inspired us to further investigate the performance between any two maximal formulas in different maximal formula groups. Our approach involves two major steps. First, we propose a new condition to compare between two different maximal formulas. Based on this new condition, we present five different scenarios under which a formula performs better than another. This is different from the condition suggested by the previous theoretical study. We performed an empirical study to compare different maximal formulas using our condition. Our results showed that among two maximal risk evaluation formulas, it is feasible to identify one that can outperform the others more frequently.

Research on Software Multiple Fault Localization Method Based on Machine Learning

Fault localization is one of time-consuming and labor-intensive activity in the debugging process. Consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with high accuracy and minimal human intervention. Despite the research of neural network and decision tree has made some progress in software multiple fault localization, there is still a lack of systematic research on various algorithms of machine learning. Therefore, a novel machine-learning-based multiple faults localization is proposed in this paper. First, several concepts and connotation of software multiple fault localization are introduced, move on to the status and development trends of the research. Next, the principles of machine learning classification algorithm are explained. Then, a software multiple fault localization research framework based on machine learning is proposed. The process is taking the Mid function as an example, compares and analyzes the performance of 22 machine learning models in software multiple fault localization. Finally, the optimal machine learning method is verified in the multiple fault localization of the Siemens suite dataset. The experimental results show that the machine learning based on Random Forest algorithm has more accuracy and significant positioning efficiency. This paper effectively solved the problem of large amount of program spectrum data and multi-coupling fault location, which is very helpful for improving the efficiency of software multiple fault debugging.

SPECTRUM

Background: Optimal usage of continuous glucose monitoring (CGM) requires adequate training of the users. Providing patients with a CGM system without such a training usually doesn’t lead to the intended improvement in metabolic control. Methods: In Germany we developed a structured training program (“SPECTRUM”) to ensure a high quality standard for the use of CGM systems. Results: This program is suitably for patients of all age groups and is applicable to all CGM systems and all forms of insulin therapy. A curriculum was also developed so that training centers with less experience with CGM could become capable of offering comprehensive CGM training. Conclusions: We believe that usage of such a program can be an important step forward in achieving more widespread acceptance and use of CGM systems. Translations in other languages and evaluation with a controlled clinical trial are planned.