Meta-Environment and Executable Meta-Language using Smalltalk: an Experience Report

Object-oriented modelling languages such as EMOF are often used to specify domain specific meta-models. However, these modelling languages lack the ability to describe behavior or operational semantics. Several approaches have used a subset of Java mixed with OCL as executable meta-languages. In this experience report we show how we use Smalltalk as an executable meta-language in the context of the Moose reengineering environment. We present how we implemented EMOF and its behavioral aspects. Over the last decade we validated this approach through incrementally building a meta-described reengineering environment. Such an approach bridges the gap between a code-oriented view and a meta-model driven one. It avoids the creation of yet another language and reuses the infrastructure and run-time of the underlying implementation language. It offers an uniform way of letting developers focus on their tasks while at the same time allowing them to meta-describe their domain model. The advantage of our approach is that developers use the same tools and environment they use for their regular tasks. Still the approach is not Smalltalk specific but can be applied to language offering an introspective API such as Ruby, Python, CLOS, Java and C#.

Capturing How Objects Flow At Runtime

Most of today's dynamic analysis approaches are based on method traces. However, in the case of object-orientation understanding program execution by analyzing method traces is complicated because the behavior of a program depends on the sharing and the transfer of object references (aliasing). We argue that trace-based dynamic analysis is at a too low level of abstraction for object-oriented systems. We propose a new approach that captures the life cycle of objects by explicitly taking into account object aliasing and how aliases propagate during the execution of the program. In this paper, we present in detail our new meta-model and discuss future tracks opened by it.

Mondrian: An Agile Visualization Framework

Data visualization is the process of representing data as pictures to support reasoning about the underlying data. For the interpretation to be as easy as possible, we need to be as close as possible to the original data. As most visualization tools have an internal meta-model, which is different from the one for the presented data, they usually need to duplicate the original data to conform to their meta-model. This leads to an increase in the resources needed, increase which is not always justified. In this work we argue for the need of having an engine that is as close as possible to the data and we present our solution of moving the visualization tool to the data, instead of moving the data to the visualization tool. Our solution also emphasizes the necessity of reusing basic blocks to express complex visualizations and allowing the programmer to script the visualization using his preferred tools, rather than a third party format. As a validation of the expressiveness of our framework, we show how we express several already published visualizations and describe the pros and cons of the approach.

Enriching Reverse Engineering with Annotations

Much of the knowledge about software systems is implicit, and therefore difficult to recover by purely automated techniques. Architectural layers and the externally visible features of software systems are two examples of information that can be difficult to detect from source code alone, and that would benefit from additional human knowledge. Typical approaches to reasoning about data involve encoding an explicit meta-model and expressing analyses at that level. Due to its informal nature, however, human knowledge can be difficult to characterize up-front and integrate into such a meta-model. We propose a generic, annotation-based approach to capture such knowledge during the reverse engineering process. Annotation types can be iteratively defined, refined and transformed, without requiring a fixed meta-model to be defined in advance. We show how our approach supports reverse engineering by implementing it in a tool called Metanool and by applying it to (i) analyzing architectural layering, (ii) tracking reengineering tasks, (iii) detecting design flaws, and (iv) analyzing features.

How Lisp systems look different

Many reverse engineering approaches have been developed to analyze software systems written in different languages like C/C++ or Java. These approaches typically rely on a meta-model, that is either specific for the language at hand or language independent (e.g. UML). However, one language that was hardly addressed is Lisp. While at first sight it can be accommodated by current language independent meta-models, Lisp has some unique features (e.g. macros, CLOS entities) that are crucial for reverse engineering Lisp systems. In this paper we propose a suite of new visualizations that reveal the special traits of the Lisp language and thus help in understanding complex Lisp systems. To validate our approach we apply them on several large Lisp case studies, and summarize our experience in terms of a series of recurring visual patterns that we have detected.

Enabling the evolution of J2EE applications through reverse engineering and quality assurance

Enterprise Applications are complex software systems that manipulate much persistent data and interact with the user through a vast and complex user interface. In particular applications written for the Java 2 Platform, Enterprise Edition (J2EE) are composed using various technologies such as Enterprise Java Beans (EJB) or Java Server Pages (JSP) that in turn rely on languages other than Java, such as XML or SQL. In this heterogeneous context applying existing reverse engineering and quality assurance techniques developed for object-oriented systems is not enough. Because those techniques have been created to measure quality or provide information about one aspect of J2EE applications, they cannot properly measure the quality of the entire system. We intend to devise techniques and metrics to measure quality in J2EE applications considering all their aspects and to aid their evolution. Using software visualization we also intend to inspect to structure of J2EE applications and all other aspects that can be investigate through this technique. In order to do that we also need to create a unified meta-model including all elements composing a J2EE application.

Body mass index, hormone replacement therapy, and endometrial cancer risk: a meta-analysis

Background: Body mass index (BMI) is a risk factor for endometrial cancer. We quantified the risk and investigated whether the association differed by use of hormone replacement therapy (HRT), menopausal status, and histologic type. Methods: We searched MEDLINE and EMBASE (1966 to December 2009) to identify prospective studies of BMI and incident endometrial cancer. We did random-effects meta-analyses, meta-regressions, and generalized least square regressions for trend estimations assuming linear, and piecewise linear, relationships. Results: Twenty-four studies (17,710 cases) were analyzed; 9 studies contributed to analyses by HRT, menopausal status, or histologic type, all published since 2003. In the linear model, the overall risk ratio (RR) per 5 kg/m2 increase in BMI was 1.60 (95% CI, 1.52–1.68), P < 0.0001. In the piecewise model, RRs compared with a normal BMI were 1.22 (1.19–1.24), 2.09 (1.94–2.26), 4.36 (3.75–5.10), and 9.11 (7.26–11.51) for BMIs of 27, 32, 37, and 42 kg/m2, respectively. The association was stronger in never HRT users than in ever users: RRs were 1.90 (1.57–2.31) and 1.18 (95% CI, 1.06–1.31) with P for interaction ¼ 0.003. In the piecewise model, the RR in never users was 20.70 (8.28–51.84) at BMI 42 kg/m2, compared with never users at normal BMI. The association was not affected by menopausal status (P ¼ 0.34) or histologic type (P ¼ 0.26). Conclusions: HRT use modifies the BMI-endometrial cancer risk association. Impact: These findings support the hypothesis that hyperestrogenia is an important mechanism underlying the BMI-endometrial cancer association, whilst the presence of residual risk in HRT users points to the role of additional systems. Cancer Epidemiol Biomarkers Prev; 19(12); 3119–30.

Efficacy of experimental treatments compared with standard treatments in non-inferiority trials: a meta-analysis of randomized controlled trials

Background There is concern that non-inferiority trials might be deliberately designed to conceal that a new treatment is less effective than a standard treatment. In order to test this hypothesis we performed a meta-analysis of non-inferiority trials to assess the average effect of experimental treatments compared with standard treatments. Methods One hundred and seventy non-inferiority treatment trials published in 121 core clinical journals were included. The trials were identified through a search of PubMed (1991 to 20 February 2009). Combined relative risk (RR) from meta-analysis comparing experimental with standard treatments was the main outcome measure. Results The 170 trials contributed a total of 175 independent comparisons of experimental with standard treatments. The combined RR for all 175 comparisons was 0.994 [95% confidence interval (CI) 0.978–1.010] using a random-effects model and 1.002 (95% CI 0.996–1.008) using a fixed-effects model. Of the 175 comparisons, experimental treatment was considered to be non-inferior in 130 (74%). The combined RR for these 130 comparisons was 0.995 (95% CI 0.983–1.006) and the point estimate favoured the experimental treatment in 58% (n = 76) and standard treatment in 42% (n = 54). The median non-inferiority margin (RR) pre-specified by trialists was 1.31 [inter-quartile range (IQR) 1.18–1.59]. Conclusion In this meta-analysis of non-inferiority trials the average RR comparing experimental with standard treatments was close to 1. The experimental treatments that gain a verdict of non-inferiority in published trials do not appear to be systematically less effective than the standard treatments. Importantly, publication bias and bias in the design and reporting of the studies cannot be ruled out and may have skewed the study results in favour of the experimental treatments. Further studies are required to examine the importance of such bias.

Effects of glucosamine, chondroitin, or placebo in patients with osteoarthritis of hip or knee: network meta-analysis

OBJECTIVE: To determine the effect of glucosamine, chondroitin, or the two in combination on joint pain and on radiological progression of disease in osteoarthritis of the hip or knee. Design Network meta-analysis. Direct comparisons within trials were combined with indirect evidence from other trials by using a Bayesian model that allowed the synthesis of multiple time points. MAIN OUTCOME MEASURE: Pain intensity. Secondary outcome was change in minimal width of joint space. The minimal clinically important difference between preparations and placebo was prespecified at -0.9 cm on a 10 cm visual analogue scale. DATA SOURCES: Electronic databases and conference proceedings from inception to June 2009, expert contact, relevant websites. Eligibility criteria for selecting studies Large scale randomised controlled trials in more than 200 patients with osteoarthritis of the knee or hip that compared glucosamine, chondroitin, or their combination with placebo or head to head. Results 10 trials in 3803 patients were included. On a 10 cm visual analogue scale the overall difference in pain intensity compared with placebo was -0.4 cm (95% credible interval -0.7 to -0.1 cm) for glucosamine, -0.3 cm (-0.7 to 0.0 cm) for chondroitin, and -0.5 cm (-0.9 to 0.0 cm) for the combination. For none of the estimates did the 95% credible intervals cross the boundary of the minimal clinically important difference. Industry independent trials showed smaller effects than commercially funded trials (P=0.02 for interaction). The differences in changes in minimal width of joint space were all minute, with 95% credible intervals overlapping zero. Conclusions Compared with placebo, glucosamine, chondroitin, and their combination do not reduce joint pain or have an impact on narrowing of joint space. Health authorities and health insurers should not cover the costs of these preparations, and new prescriptions to patients who have not received treatment should be discouraged.

Does land-based exercise reduce pain and disability associated with hip osteoarthritis? A meta-analysis of randomized controlled trials

Objective To determine if clinical guidelines recommending therapeutic exercise for people with hip osteoarthritis (OA) are supported by rigorous scientific evidence. Methods A meta-analysis of randomized controlled trials (RCTs) recruiting people with hip OA and comparing some form of land-based exercise program (as opposed to exercises conducted in the water) with a non-exercise group in terms of hip pain and/or self-reported physical function. Results Thirty-two RCTs were identified, but only five met the inclusion criteria. Only one of the five included RCTs restricted recruitment to people with hip OA, the other four RCTs also recruiting participants with knee OA. The five included studies provided data on 204 and 187 hip OA participants for pain and physical function, respectively. Combining the results of the five included RCTs using a fixed-effects model demonstrated a small treatment effect for pain (standardized mean difference (SMD) −0.38; 95% confidence interval (CI) −0.67 to −0.09). No significant benefit in terms of improved self-reported physical function was detected (SMD −0.02; 95% CI −0.31 to 0.28). Conclusion Currently there is only silver level evidence (one small RCT) supporting the benefit of land-based therapeutic exercise for people with symptomatic hip OA in terms of reduced pain and improved physical function. The limited number and small sample size of the included RCTs restricts the confidence that can be attributed to these results.

Run-Time Evolution through Explicit Meta-Objects

Software must be constantly adapted due to evolving domain knowledge and unanticipated requirements changes. To adapt a system at run-time we need to reflect on its structure and its behavior. Object-oriented languages introduced reflection to deal with this issue, however, no reflective approach up to now has tried to provide a unified solution to both structural and behavioral reflection. This paper describes Albedo, a unified approach to structural and behavioral reflection. Albedo is a model of fined-grained unanticipated dynamic structural and behavioral adaptation. Instead of providing reflective capabilities as an external mechanism we integrate them deeply in the environment. We show how explicit meta-objects allow us to provide a range of reflective features and thereby evolve both application models and environments at run-time.

Role of hepatitis C virus genotype 3 in liver fibrosis progression - a systematic review and meta-analysis

The progression of liver fibrosis in chronic hepatitis C has long been considered to be independent from viral genotypes. However, recent studies suggest an association between Hepatitis C virus (HCV) genotype 3 and accelerated liver disease progression. We completed a systematic review and meta-analysis of studies evaluating the association between HCV genotypes and fibrosis progression. PubMed, Embase and ISI Web of Knowledge databases were searched for cohort, cross-sectional and case-control studies on treatment-naïve HCV-infected adults in which liver fibrosis progression rate (FPR) was assessed by the ratio of fibrosis stage in one single biopsy to the duration of infection (single-biopsy studies) or from the change in fibrosis stage between two biopsies (paired biopsies studies). A random effect model was used to derive FPR among different HCV genotypes. Eight single-biopsy studies (3182 patients, mean/median duration of infection ranging from 9 to 21 years) and eight paired biopsies studies (mean interval between biopsies 2-12 years) met the selection criteria. The odds ratio for the association of genotype 3 with accelerated fibrosis progression was 1.52 (95% CI 1.12-2.07, P = 0.007) in single-biopsy studies and 1.37 (95% CI 0.87-2.17, P = 0.17) in paired biopsy studies. In conclusion, viral genotype 3 was associated with faster fibrosis progression in single-biopsy studies. This observation may have important consequences on the clinical management of genotype 3-infected patients. The association was not significant in paired biopsies studies, although the latter may be limited by important indication bias, short observation time and small sample size.

Growth hormone receptor polymorphism and growth hormone therapy response in children: a bayesian meta-analysis

Recombinant human growth hormone (rhGH) therapy is used in the long-term treatment of children with growth disorders, but there is considerable treatment response variability. The exon 3-deleted growth hormone receptor polymorphism (GHR(d3)) may account for some of this variability. The authors performed a systematic review (to April 2011), including investigator-only data, to quantify the effects of the GHR(fl-d3) and GHR(d3-d3) genotypes on rhGH therapy response and used a recently established Bayesian inheritance model-free approach to meta-analyze the data. The primary outcome was the 1-year change-in-height standard-deviation score for the 2 genotypes. Eighteen data sets from 12 studies (1,527 children) were included. After several prior assumptions were tested, the most appropriate inheritance model was codominant (posterior probability = 0.93). Compared with noncarriers, carriers had median differences in 1-year change-in-height standard-deviation score of 0.09 (95% credible interval (CrI): 0.01, 0.17) for GHR(fl-d3) and of 0.14 (95% CrI: 0.02, 0.26) for GHR(d3-d3). However, the between-study standard deviation of 0.18 (95% CrI: 0.10, 0.33) was considerable. The authors tested by meta-regression for potential modifiers and found no substantial influence. They conclude that 1) the GHR(d3) polymorphism inheritance is codominant, contrasting with previous reports; 2) GHR(d3) genotypes account for modest increases in rhGH effects in children; and 3) considerable unexplained variability in responsiveness remains.

Bivariate meta-analysis of predictive values of diagnostic tests can be an alternative to bivariate meta-analysis of sensitivity and specificity

Meta-analysis of predictive values is usually discouraged because these values are directly affected by disease prevalence, but sensitivity and specificity sometimes show substantial heterogeneity as well. We propose a bivariate random-effects logitnormal model for the meta-analysis of the positive predictive value (PPV) and negative predictive value (NPV) of diagnostic tests.