Modeling Human Aspects to Enhance Software Quality Management

The aim of the research is to explore the impact of cognitive biases and social networks in testing and developing software. The research will aim to address two critical areas: i) to predict defective parts of the software, ii) to determine the right person to test the defective parts of the software. Every phase in software development requires analytical problem solving skills. Moreover, using everyday life heuristics instead of laws of logic and mathematics may affect quality of the software product in an undesirable manner. The proposed research aims to understand how mind works in solving problems. People also work in teams in software development that their social interactions in solving a problem may affect the quality of the product. The proposed research also aims to model the social network structure of testers and developers to understand their impact on software quality and defect prediction performance

Is There Need for a New Hepatitıs B Vaccine Schedule for Children with Celiac Disease?

Background: Celiac disease (CD) is an autoimmune disease characterized by immune-mediated inflammatory damage of the small intestinal mucosa, precipitated by the ingestion of gluten-containing foods. Since human leucocyte antigen DQ2 (HLA-DQ2) is a marker of nonresponsiveness to hepatits B virus (HBV) vaccine, CD may also be associated with this nonresponsiveness

Dione: An Integrated Measurement and Defect Prediction Solution

We present an integrated measurement and defect prediction tool: Dione. Our tool enables organizations to measure, monitor, and control product quality through learning based defect prediction. Similar existing tools either provide data collection and analytics, or work just as a prediction engine. Therefore, companies need to deal with multiple tools with incompatible interfaces in order to deploy a complete measurement and prediction solution. Dione provides a fully integrated solution where data extraction, defect prediction and reporting steps fit seamlessly. In this paper, we present the major functionality and architectural elements of Dione followed by an overview of our demonstration

Analyzing the concept of technical debt in the context of agile software development: A systematic literature review

Technical debt (TD) is a metaphor that is used to communicate the consequences of poor software development practices to non-technical stakeholders. In recent years, it has gained significant attention in agile software development (ASD). The purpose of this study is to analyze and synthesize the state of the art of TD, and its causes, consequences, and management strategies in the context of ASD. Using a systematic literature review (SLR), 38 primary studies, out of 346 studies, were identified and analyzed. We found five research areas of interest related to the literature of TD in ASD. Among those areas, managing TD in ASD received the highest attention, followed by architecture in ASD and its relationship with TD. In addition, eight categories regarding the causes and five categories regarding the consequences of incurring TD in ASD were identified. Focus on quick delivery and architectural and design issues were the most popular causes of incurring TD in ASD. Reduced productivity, system degradation and increased maintenance cost were identified as significant consequences of incurring TD in ASD. Additionally, we found 12 strategies for managing TD in the context of ASD, out of which refactoring and enhancing the visibility of TD were the most significant. The results of this study provide a structured synthesis of TD and its management in the context of ASD as well as potential research areas for further investigation

Detection of Rare Events: Cluster Based Preprocessing of the Training Set: The Case on Complaints for Invoice Time Series

Detection of rare events is a major problem when dealing with unbalanced data. In the application of machine learning tools, data is split into training and test samples and preprocessing is applied to the training set, with the aim of obtaining a more balanced sample. In this paper we discuss preprocessing methods applied to heterogenous data clustered with respect to expected anomaly types. We propose a method for deciding on oversampling and under-sampling from each cluster, based on the variability of the items in each cluster, using Principal Component Analysis. The method is applied to the problem of detecting anomalies in a time series invoices, with an average rate of complaints of orders 10-4.

Topic selection in industry experiments

This paper shares our experience with initial negotiation and topic elicitation process for conducting industry experiments in six software development organizations in Finland. The process involved interaction with company representatives in the form of both multiple group discussions and separate face-to-face meetings. Fitness criteria developed by researchers were applied to the list of generated topics to decide on a common topic. The challenges we faced include diversity of proposed topics, communication gaps, skepticism about research methods, initial disconnect between research and industry needs, and lack of prior work relationship. Lessons learned include having enough time to establish trust with partners, importance of leveraging the benefits of training and skill development that are inherent in the experimental approach, uniquely positioning the experimental approach within the landscape of other validation approaches more familiar to industrial partners, and introducing the fitness criteria early in the process

Correlations between autonomic dysfunction and circadian changes and arrhythmia prevalence in women with fibromyalgia syndrome

WOS: 000265395700007PubMed: 19357052Objective: It is known that increased sympathetic activity and decreased parasympathetic activity are present in patients with fibromyalgia syndrome (FMS). This study aims to investigate the correlations of autonomic dysfunction and differences in autonomic circadian activity with arrhythmia prevalence in women with FMS. Methods: Fifty female patients with FMS and 30 healthy female controls were included in this cross-sectional, case-controlled study. A 12-lead electrocardiogram and 24-hour Holter monitoring were performed in all patients to evaluate arrhythmias and autonomic function tests. Heart rate variability (HRV) parameters were utilized to detect autonomic dysfunction in patients with FMS. HRV measurements were performed in total 24-hour, day time (06:00-22:59), night time (23:00-05:59) periods and during autonomic tests (stand - supine, inspiration-expiration and Valsalva tests) using 24-hour Holter monitoring recordings. Student t-test, Mann-Whitney U and Pearson Chi-square tests were used for comparisons of the data between groups. The correlation of data was tested by using Spearman correlation analysis. Results: The mean ages of the patient and control groups were 38 +/- 7.4 and 36 +/- 8.1 years, respectively. In HRV measurements, high frequency (HF) power, was significantly decreased in the patient group as compared with control group (167.4 msec(2) (107.0-312.0) vs.314.5 msec(2) (124.0-905.0), p=0.017). The low frequency/HF ratio (LF/HF) values for total 24 hours (2.22 +/- 0.18 vs. 1.22 +/- 0.12, p<0.001) and in the night time period (2.78 +/- 1.97 vs.1.15 +/- 0.77, p<0.001) were found to be significantly higher in the patient group than in control one. The ratio of LF/HFDay/LF/HFNight was markedly higher in the control group (2.67 (1.22-5.65) vs. 1.45 (0.83-2.05), p=0.004). The prevalence (p=0.028) and total number (127.1 +/- 21.4 vs. 187.3 +/- 62.3, p=0.019) of supraventricular extrasystoles in 24-hour period was higher in the patient group. Conclusion: The sympathetic activity was significantly increased and parasympathetic activity significantly decreased in FMS patients. Additionally, significant autonomic circadian activity changes were also detected in these patients. These autonomic changes might be linked to increased arrhythmia prevalence. (Anadolu Kardiyol Derg 2009, 9: 110-7

Empirical evaluation of the effects of experience on code quality and programmer productivity: an exploratory study

There is a widespread belief in both SE and other branches of science that experience helps professionals to improve their performance. However, cases have been reported where experience not only does not have a positive influence but sometimes even degrades the performance of professionals