A Nano-Pattern Language for Java

Gil, Yossi; Marcovitch, Ori; Orrù, Marisa

doi:10.1016/j.cola.2019.100905

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…Our direct future work includes: 1) improving POS taggers for source code by leveraging ensemble tagging [64], 2) using grammar patterns and static analysis models [25,65,26] to appraise identifier names, and 3) studying grammar patterns in specific design/architectural patterns and on test code. The results from this work will be particularly useful for improving the analysis of words, and their relationship to other nearby words, in the lexicon of a given software system.…”

Section: Discussionmentioning

confidence: 99%

On the generation, structure, and semantics of grammar patterns in source code identifiers

Newman

Alsuhaibani

Decker

et al. 2020

Journal of Systems and Software

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Identifiers make up a majority of the text in code. They are one of the most basic mediums through which developers describe the code they create and understand the code that others create. Therefore, understanding the patterns latent in identifier naming practices and how accurately we are able to automatically model these patterns is vital if researchers are to support developers and automated analysis approaches in comprehending and creating identifiers correctly and optimally. This paper investigates identifiers by studying sequences of partof-speech annotations, referred to as grammar patterns. This work advances our understanding of these patterns and our ability to model them by 1) establishing common naming patterns in different types of identifiers, such as class and attribute names; 2) analyzing how different patterns influence comprehension; and 3) studying the accuracy of state-of-the-art techniques for part-of-speech annotations, which are vital in automatically modeling identifier naming patterns, in order to establish their limits and paths toward improvement. To do this, we manually annotate a dataset of 1,335 identifiers from 20 open-source systems and use this dataset to study naming patterns, semantics, and tagger

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Section: Discussionmentioning

confidence: 99%

On the generation, structure, and semantics of grammar patterns in source code identifiers

Newman

Alsuhaibani

Decker

et al. 2020

Journal of Systems and Software

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show abstract

“…We added the new features carefully, using the same versions of the projects as in the original GPG dataset. For the sake of reproducibility, we ran a mining tool, which is part of the open-source Spartanizer project, used in previous studies [48], [49]. Here, we modified it slightly to meet our needs.…”

Section: A Lighter Set Of Featuresmentioning

confidence: 99%

“…We also extended the data obtained by GPG with other features, as explained above. For mining these new features, we used the Spartanizer API, which has been used in the past for empirical software engineering studies [48], [49]. To validate the reliability of the mining, we manually sampled 10% of the test classes and 5% of the ASTs of test classes and made sure the automatic mining was accurate.…”

Section: Threats To Construct Validitymentioning

confidence: 99%

Better Prediction of Mutation Score

Gil¹,

Ma’ayan²

2021

Preprint

Self Cite

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<div><div><div><p>Mutation score is widely accepted to be a reliable measurement for the effectiveness of software tests. Recent studies, however, show that mutation analysis is extremely costly and hard to use in practice. We present a novel direct prediction model of mutation score using neural networks. Relying solely on static code features that do not require generation of mutants or execution of the tests, we predict mutation score with an accuracy better than a quintile. When we include statement coverage as a feature, our accuracy rises to about a decile. Using a similar approach, we also improve the state-of-the-art results for binary test effectiveness prediction and introduce an intuitive, easy-to-calculate set of features superior to previously studied sets. We also publish the largest dataset of test-class level mutation score and static code features data to date, for future research. Finally, we discuss how our approach could be integrated into real-world systems, IDEs, CI tools, and testing frameworks.</p></div></div></div>

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“…We restrict our research to the original “Gang‐of‐four” patterns, exclusing other developments in patterns such as nano patterns in programming ccode, 12 patterns for different computing models, 13 or security patterns 14 …”

Section: Introductionmentioning

confidence: 99%

“…We restrict our research to the original "Gang-of-four" patterns, exclusing other developments in patterns such as nano patterns in programming ccode, 12 patterns for different computing models, 13 or security patterns. 14 Our main contribution is that we reason, in a systematic way, about how to compare possible implementations of design patterns.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of design pattern alternatives in Java

et al. 2021

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Design patterns are standard solutions to common design problems. The famous Gang of Four book describes more than twenty design patterns for the object-oriented paradigm. These patterns were developed more than twenty-five years ago, using the programming language concepts available at that time. Patterns do not always fit underlying domain concepts. For example, even when a concrete strategy is a pure function, the classical strategy pattern represents this as a separate subclass and as such obscures the intent of this pattern with extra complexities due to the inheritance-based implementation. Due to the ongoing development of oo-languages, a relevant question is whether the implementation of these patterns can be improved using new language features, such that they fit more closely with the intent. An additional question is then how we can decide which implementation is to be preferred. In this article, we investigate both questions, using the strategy pattern as an example. Our main contribution is that we show how to reason about different implementations, using both the description of a design pattern and design principles as guidance.

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A Nano-Pattern Language for Java

Cited by 7 publications

References 25 publications

On the generation, structure, and semantics of grammar patterns in source code identifiers

On the generation, structure, and semantics of grammar patterns in source code identifiers

Better Prediction of Mutation Score

Evaluation of design pattern alternatives in Java

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