Quality Assessment and Prediction in Software Product Lines

At the heart of product line development is the assumption that through structured reuse later products will be of a higher quality and require less time and effort to develop and test. This thesis presents empirical results from two case studies aimed at assessing the quality aspect of this claim and exploring fault prediction in the context of software product lines. The first case study examines pre-release faults and change proneness of four products in PolyFlow, a medium-sized, industrial software product line; the second case study analyzes post-release faults using pre-release data over seven releases of four products in Eclipse, a very large, open source software product line.;The goals of our research are (1) to determine the association between various software metrics, as well as their correlation with the number of faults at the component/package level; (2) to characterize the fault and change proneness of components/packages at various levels of reuse; (3) to explore the benefits of the structured reuse found in software product lines; and (4) to evaluate the effectiveness of predictive models, built on a variety of products in a software product line, to make accurate predictions of pre-release software faults (in the case of PolyFlow) and post-release software faults (in the case of Eclipse).;The research results of both studies confirm, in a software product line setting, the findings of others that faults (both pre- and post-release) are more highly correlated to change metrics than to static code metrics, and are mostly contained in a small set of components/ packages. The longitudinal aspect of our research indicates that new products do benefit from the development and testing of previous products. The results also indicate that pre-existing components/packages, including the common components/packages, undergo continuous change, but tend to sustain low fault densities. However, this is not always true for newly developed components/packages. Finally, the results also show that predictions of pre-release faults in the case of PolyFlow and post-release faults in the case of Eclipse can be done accurately from pre-release data, and furthermore, that these predictions benefit from information about additional products in the software product lines

Searching for Needles in the Cosmic Haystack

Searching for pulsar signals in radio astronomy data sets is a difficult task. The data sets are extremely large, approaching the petabyte scale, and are growing larger as instruments become more advanced. Big Data brings with it big challenges. Processing the data to identify candidate pulsar signals is computationally expensive and must utilize parallelism to be scalable. Labeling benchmarks for supervised classification is costly. To compound the problem, pulsar signals are very rare, e.g., only 0.05% of the instances in one data set represent pulsars. Furthermore, there are many different approaches to candidate classification with no consensus on a best practice. This dissertation is focused on identifying and classifying radio pulsar candidates from single pulse searches. First, to identify and classify Dispersed Pulse Groups (DPGs), we developed a supervised machine learning approach that consists of RAPID (a novel peak identification algorithm), feature extraction, and supervised machine learning classification. We tested six algorithms for classification with four imbalance treatments. Results showed that classifiers with imbalance treatments had higher recall values. Overall, classifiers using multiclass RandomForests combined with Synthetic Majority Oversampling TEchnique (SMOTE) were the most efficient; they identified additional known pulsars not in the benchmark, with less false positives than other classifiers. Second, we developed a parallel single pulse identification method, D-RAPID, and introduced a novel automated multiclass labeling (ALM) technique that we combined with feature selection to improve execution performance. D-RAPID improved execution performance over RAPID by a factor of 5. We also showed that the combination of ALM and feature selection sped up the execution performance of RandomForest by 54% on average with less than a 2% average reduction in classification performance. Finally, we proposed CoDRIFt, a novel classification algorithm that is distributed for scalability and employs semi-supervised learning to leverage unlabeled data to inform classification. We evaluated and compared CoDRIFt to eleven other classifiers. The results showed that CoDRIFt excelled at classifying candidates in imbalanced benchmarks with a majority of non-pulsar signals (\u3e95%). Furthermore, CoDRIFt models created with very limited sets of labeled data (as few as 22 labeled minority class instances) were able to achieve high recall (mean = 0.98). In comparison to the other algorithms trained on similar sets, CoDRIFt outperformed them all, with recall 2.9% higher than the next best classifier and a 35% average improvement over all eleven classifiers. CoDRIFt is customizable for other problem domains with very large, imbalanced data sets, such as fraud detection and cyber attack detection

Detection of dispersed radio pulses: a machine learning approach to candidate identification and classification

Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a new, two-stage approach for identifying and classifying dispersed pulse groups (DPGs) in single-pulse search output. The first stage identified DPGs and extracted features to characterize them using a new peak identification algorithm which tracks sloping tendencies around local maxima in plots of signal-to-noise ratio versus dispersion measure. The second stage used supervised machine learning to classify DPGs. We created four benchmark data sets: one unbalanced and three balanced versions using three different imbalance treatments. We empirically evaluated 48 classifiers by training and testing binary and multiclass versions of six machine learning algorithms on each of the four benchmark versions. While each classifier had advantages and disadvantages, all classifiers with imbalance treatments had higher recall values than those with unbalanced data, regardless of the machine learning algorithm used. Based on the benchmarking results, we selected a subset of classifiers to classify the full, unlabelled data set of over 1.5 million DPGs identified in 42 405 observations made by the Green Bank Telescope. Overall, the classifiers using a multiclass ensemble tree learner in combination with two oversampling imbalance treatments were the most efficient; they identified additional known pulsars not in the benchmark data set and provided six potential discoveries, with significantly less false positives than the other classifiers

Patterns and frequency of anxiety in women undergoing gynaecological surgery

Patterns and frequency of anxiety in women undergoing gynaecological surgery Aims. Within a gynaecological surgical setting to identify the patterns and frequency of anxiety pre- and postoperatively; to identify any correlation between raised anxiety levels and postoperative pain; to identify events, from the patients’ perspective, that may increase or decrease anxiety in the pre- and postoperative periods. Background. It is well documented that surgery is associated with increased anxiety, which has an adverse impact on patient outcomes. Few studies have been conducted to obtain the patient’s perspective on the experience of anxiety and the events and situations that aggravate and ameliorate it. Method. The study used a mixed method approach. The sample consisted of women undergoing planned gynaecological surgery. Anxiety was assessed using the State Trait Anxiety Inventory. Trait anxiety was measured at the time of recruitment. State anxiety was then assessed at six time points during the pre- and postoperative periods. Postoperative pain was also measured using a 10 cm visual analogue scale. Taped semi-structured telephone interviews were conducted approximately a week after discharge. Results. State anxiety rose steadily from the night before surgery to the point of leaving the ward to go to theatre. Anxiety then increased sharply prior to the anaesthetic decreasing sharply afterwards. Patients with higher levels of trait anxiety were more likely to experience higher levels of anxiety throughout their admission. Elevated levels of pre- and postoperative anxiety were associated with increased levels of postoperative pain. Telephone interviews revealed a range of events/situations that patients recalled distressing them and many were related to inadequate information. Conclusion. This study found higher rates of anxiety than previously reported and anxiety levels appeared raised before admission to hospital. This has important clinical and research implications.Relevance to clinical practice. Patients with high levels of anxiety may be identified preoperatively and interventions designed to reduce anxiety could be targeted to this vulnerable group. Patient experiences can inform the delivery of services to meet their health needs better

An umbrella review of the benefits and risks associated with youths’ interactions with electronic screens

The influence of electronic screens on the health of children and adolescents and their education is not well understood. In this prospectively registered umbrella review (PROSPERO identifier CRD42017076051), we harmonized effects from 102 meta-analyses (2,451 primary studies; 1,937,501 participants) of screen time and outcomes. In total, 43 effects from 32 meta-analyses met our criteria for statistical certainty. Meta-analyses of associations between screen use and outcomes showed small-to-moderate effects (range: r = –0.14 to 0.33). In education, results were mixed; for example, screen use was negatively associated with literacy (r = –0.14, 95% confidence interval (CI) = –0.20 to –0.09, P ≤ 0.001, k = 38, N = 18,318), but this effect was positive when parents watched with their children (r = 0.15, 95% CI = 0.02 to 0.28, P = 0.028, k = 12, N = 6,083). In health, we found evidence for several small negative associations; for example, social media was associated with depression (r = 0.12, 95% CI = 0.05 to 0.19, P ≤ 0.001, k = 12, N = 93,740). Limitations of our review include the limited number of studies for each outcome, medium-to-high risk of bias in 95 out of 102 included meta-analyses and high heterogeneity (17 out of 22 in education and 20 out of 21 in health with I2 > 50%). We recommend that caregivers and policymakers carefully weigh the evidence for potential harms and benefits of specific types of screen use

Developmental regulation of tau splicing is disrupted in stem cell-derived neurons from frontotemporal dementia patients with the 10 + 16 splice-site mutation in MAPT

The alternative splicing of the tau gene, MAPT, generates six protein isoforms in the adult human CNS. Tau splicing is developmentally regulated and dysregulated in disease. Mutations in MAPT that alter tau splicing cause frontotemporal dementia (FTD) with tau pathology, providing evidence for a causal link between altered tau splicing and disease. The use of induced pluripotent stem cell (iPSC) derived neurons has revolutionized the way we model neurological disease in vitro. However, as most tau mutations are located within or around the alternatively spliced exon 10, it is important that iPSC-neurons splice tau appropriately in order to be used as disease models. To address this issue, we analysed the expression, and splicing of tau in iPSC-derived cortical neurons from control patients and FTD patients with the 10+16 intronic mutation in MAPT. We show that control neurons only express the fetal tau isoform (0N3R), even at extended time points of 100 days in vitro. Neurons from FTD patients with the 10+16 mutation in MAPT express both 0N3R and 0N4R tau isoforms, demonstrating that this mutation overrides the developmental regulation of exon 10 inclusion in our in vitro model. Further, at extended time-points of 365 days in vitro, we observe a switch in tau splicing to include six tau isoforms as seen the adult human CNS. Our results demonstrate the importance of neuronal maturity for use in in vitro modeling and provide a system that will be important for understanding the functional consequences of altered tau splicing

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin