Maximising the information gained from an experimental analysis of code inspection and static analysis for concurrent Java components

The results of empirical studies are limited to particular contexts, difficult to generalise and the studies themselves are expensive to perform. Despite these problems, empirical studies in software engineering can be made effective and they are important to both researchers and practitioners. The key to their effectiveness lies in the maximisation of the information that can be gained by examining existing studies, conducting power analyses for an accurate minimum sample size and benefiting from previous studies through replication. This approach was applied in a controlled experiment examining the combination of automated static analysis tools and code inspection in the context of verification and validation (V&V) of concurrent Java components. The combination of these V&V technologies was shown to be cost-effective despite the size of the study, which thus contributes to research in V&V technology evaluation.

Levonorgestrel intrauterine system versus medical therapy for menorrhagia

Background - Menorrhagia is a common problem, yet evidence to inform decisions about therapy is limited. In a pragmatic, multicenter, randomized trial, we compared the levonorgestrel-releasing intrauterine system (levonorgestrel-IUS) with usual medical treatment in women with menorrhagia who presented to their primary care providers. Methods - We randomly assigned 571 women with menorrhagia to treatment with levonorgestrel-IUS or usual medical treatment (tranexamic acid, mefenamic acid, combined estrogen–progestogen, or progesterone alone). The primary outcome was the patient-reported score on the Menorrhagia Multi-Attribute Scale (MMAS) (ranging from 0 to 100, with lower scores indicating greater severity), assessed over a 2-year period. Secondary outcomes included general quality-of-life and sexual-activity scores and surgical intervention. Results - MMAS scores improved from baseline to 6 months in both the levonorgestrel-IUS group and the usual-treatment group (mean increase, 32.7 and 21.4 points, respectively; P<0.001 for both comparisons). The improvements were maintained over a 2-year period but were significantly greater in the levonorgestrel-IUS group than in the usual-treatment group (mean between-group difference, 13.4 points; 95% confidence interval, 9.9 to 16.9; P<0.001). Improvements in all MMAS domains (practical difficulties, social life, family life, work and daily routine, psychological well-being, and physical health) were significantly greater in the levonorgestrel-IUS group than in the usual-treatment group, and this was also true for seven of the eight quality-of-life domains. At 2 years, more of the women were still using the levonorgestrel-IUS than were undergoing the usual medical treatment (64% vs. 38%, P<0.001). There were no significant between-group differences in the rates of surgical intervention or sexual-activity scores. There were no significant differences in serious adverse events between groups. Conclusions - In women with menorrhagia who presented to primary care providers, the levonorgestrel-IUS was more effective than usual medical treatment in reducing the effect of heavy menstrual bleeding on quality of life. (Funded by the National Institute of Health Research Health Technology Assessment Programme; ECLIPSE Controlled-Trials.com number, ISRCTN86566246.)

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.