Automatic Labeling of Software Components and their Evolution using Log-Likelihood Ratio of Word Frequencies in Source Code

As more and more open-source software components become available on the internet we need automatic ways to label and compare them. For example, a developer who searches for reusable software must be able to quickly gain an understanding of retrieved components. This understanding cannot be gained at the level of source code due to the semantic gap between source code and the domain model. In this paper we present a lexical approach that uses the log-likelihood ratios of word frequencies to automatically provide labels for software components. We present a prototype implementation of our labeling/comparison algorithm and provide examples of its application. In particular, we apply the approach to detect trends in the evolution of a software system.

Bounded Seas: Island Parsing Without Shipwrecks

Imprecise manipulation of source code (semi-parsing) is useful for tasks such as robust parsing, error recovery, lexical analysis, and rapid development of parsers for data extraction. An island grammar precisely defines only a subset of a language syntax (islands), while the rest of the syntax (water) is defined imprecisely. Usually, water is defined as the negation of islands. Albeit simple, such a definition of water is naive and impedes composition of islands. When developing an island grammar, sooner or later a programmer has to create water tailored to each individual island. Such an approach is fragile, however, because water can change with any change of a grammar. It is time-consuming, because water is defined manually by a programmer and not automatically. Finally, an island surrounded by water cannot be reused because water has to be defined for every grammar individually. In this paper we propose a new technique of island parsing - bounded seas. Bounded seas are composable, robust, reusable and easy to use because island-specific water is created automatically. We integrated bounded seas into a parser combinator framework as a demonstration of their composability and reusability.

Treatment intensification with insulin glargine in patients with inadequately controlled type 2 diabetes improves glycaemic control with a high treatment satisfaction and no weight gain

PRINCIPLES We aimed to evaluate the efficacy of, and treatment satisfaction with, insulin glargine administered with SoloSTAR® or ClikSTAR® pens in patients with type 2 diabetes mellitus managed by primary care physicians in Switzerland. METHODS A total of 327 patients with inadequately controlled type 2 diabetes were enrolled by 72 physicians in this prospective observational study, which aimed to evaluate the efficacy of a 6-month course of insulin glargine therapy measured as development of glycaemic control (glycosylated haemoglobin [HbA1c] and fasting plasma glucose [FPG]) and weight change. We also assessed preference for reusable or disposable pens, and treatment satisfaction. RESULTS After 6 months, the mean daily dose of insulin glargine was 27.7±14.3 U, and dose titration was completed in 228 (72.4%) patients. Mean HbA1c decreased from 8.9%±1.6% (n=327) to 7.3%±1.0% (n=315) (p<0.0001), and 138 (43.8%) patients achieved an HbA1c≤7.0%. Mean FPG decreased from 10.9±4.5 to 7.3±1.8 mmol/l (p<0.0001). Mean body weight did not change (85.4±17.2 kg vs 85.0±16.5 kg; p=0.11). Patients' preference was in favour of the disposable SoloStar® pen (80%), as compared with the reusable ClickStar® pen (20%). Overall, 92.6% of physicians and 96.3% of patients were satisfied or very satisfied with the insulin glargine therapy. CONCLUSIONS In patients with type 2 diabetes insulin glargine administered by SoloSTAR® or ClikSTAR® pens, education on insulin injection and on self-management of diabetes was associated with clinically meaningful improvements in HbA1c and FPG without a mean collective weight gain. The vast majority of both patients and primary care physicians were satisfied with the treatment intensification.

Bounded seas

Abstract Imprecise manipulation of source code (semi-parsing) is useful for tasks such as robust parsing, error recovery, lexical analysis, and rapid development of parsers for data extraction. An island grammar precisely defines only a subset of a language syntax (islands), while the rest of the syntax (water) is defined imprecisely. Usually water is defined as the negation of islands. Albeit simple, such a definition of water is naive and impedes composition of islands. When developing an island grammar, sooner or later a language engineer has to create water tailored to each individual island. Such an approach is fragile, because water can change with any change of a grammar. It is time-consuming, because water is defined manually by an engineer and not automatically. Finally, an island surrounded by water cannot be reused because water has to be defined for every grammar individually. In this paper we propose a new technique of island parsing —- bounded seas. Bounded seas are composable, robust, reusable and easy to use because island-specific water is created automatically. Our work focuses on applications of island parsing to data extraction from source code. We have integrated bounded seas into a parser combinator framework as a demonstration of their composability and reusability.