Enabling PHP software engineering research in Rascal

Hills, Mark; Klint, Paul; Vinju, Jurgen

doi:10.1016/j.scico.2016.05.003

Cited by 12 publications

(4 citation statements)

References 33 publications

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“…Considering the database structure, three SQL algorithms were used to sort estimated Euclidean distances. These algorithms use various mechanisms called: MergeSort, QuickSort and HeapSort [32][33][34][35]. They were automatically selected according to the database filtering method (optionally according to the product ERP index and/or production line number) taking into account different weight values [28,32].…”

Section: Resultsmentioning

confidence: 99%

Management and Production Engineering Review

Sika

Szajewski

Hajkowski

et al. 2019

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The paper presents an example of Instance-Based Learning using a supervised classification method of predicting selected ductile cast iron castings defects. The test used the algorithm of k-nearest neighbours, which was implemented in the authors' computer application. To ensure its proper work it is necessary to have historical data of casting parameter values registered during casting processes in a foundry (mould sand, pouring process, chemical composition) as well as the percentage share of defective castings (unrepairable casting defects). The result of an algorithm is a report with five most possible scenarios in terms of occurrence of a cast iron casting defects and their quantity and occurrence percentage in the casts series. During the algorithm testing, weights were adjusted for independent variables involved in the dependent variables learning process. The algorithms used to process numerous data sets should be characterized by high efficiency, which should be a priority when designing applications to be implemented in industry. As it turns out in the presented mathematical instance-based learning, the best quality of fit occurs for specific values of accepted weights (set #5) for number k = 5 nearest neighbours and taking into account the search criterion according to "product index".

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Section: Resultsmentioning

confidence: 99%

Management and Production Engineering Review

Sika

Szajewski

Hajkowski

et al. 2019

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“…With PHP being a rather dynamic language, there is a spectrum of tools for it using dynamic analysis [16], [18], static analysis or a combination thereof. Since we prefer to stay with static analysis as far as possible, the most related existing solutions for us are PhpStorm [9], PHP AiR [7], [8] and the unnamed framework by Hauzar and Kofron [6]. Most PHP code analysis tools have a strong focus on standard enforcing (e.g., smell detection) and security (including instrumentation).…”

Section: Related Workmentioning

confidence: 99%

“…In the study by Hills et al [8], statistical analysis was performed on feature usage in various open source projects. Various interesting insights and conclusions come forward, but no comments are made on the modernity, or lack thereof, of the code in the corpus.…”

Section: Related Workmentioning

confidence: 99%

Deriving Modernity Signatures for PHP Systems with Static Analysis

Brink

Gerhold

Zaytsev

2022

2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM)

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The PHP language has undergone many changes in its syntax and grammar, with respect to both features the language has to offer as well as the distribution of language features used by programmers in their projects. We present a novel method of using grammar usage statistics to calculate a modernity signature for a PHP system, so that we can determine its age. The system will aid developers in choosing whether or not to execute or use a PHP system, without having to perform an extensive inspection.

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“…Recently, Hills et al have described a framework, PHP AiR , which is written in Rascal and designed to act as a foundation for research in PHP software engineering . Their work has a similar motivation to ours, since they wish to use their framework as a basis for empirical studies of PHP programs, including tracking the evolution of language features over time.…”

Section: Background and Related Work About Grammar Engineeringmentioning

confidence: 99%

Grammar engineering for multiple front‐ends for Python

Malloy

Power

2018

Softw Pract Exp

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In this paper, we describe our experience in grammar engineering to construct multiple parsers and front ends for the Python language. We present a metrics-based study of the evolution of the Python grammars through the multiple versions of the language in an effort to distinguish and measure grammar evolution and to provide a basis of comparison with related research in grammar engineering. To conduct this research, we have built a toolkit, pygrat, which builds on tools developed in other research. We use pygrat to build a system that automates much of the process needed to translate the Python grammars from EBNF to a formalism acceptable to the bison parser generator. We exploit the suite of Python test cases, used by the Python developers, to validate our parser generation. Finally, we describe our use of the menhir parser generator to facilitate the parser and front-end construction, eliminating some of the transformations and providing practical support for grammar modularisation.

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Enabling PHP software engineering research in Rascal

Cited by 12 publications

References 33 publications

Management and Production Engineering Review

Management and Production Engineering Review

Deriving Modernity Signatures for PHP Systems with Static Analysis

Grammar engineering for multiple front‐ends for Python

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