Measuring the quality of diff algorithms: a formalization

Barabucci, Gioele; Ciancarini, Paolo; Iorio, Angelo Di; Vitali, Fabio

doi:10.1016/j.csi.2015.12.005

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…The second approach is empirical, analyzing particular computer resources usage through space and time efficiency (or space complexity, the memory required). Objective and informative metrics should accompany each approach [ 2 , 31 , 32 , 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Entropy-Based Approach in Selection Exact String-Matching Algorithms

Markić

Štula

Zoric

et al. 2020

Entropy

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The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns’ entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology.

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Section: Methodsmentioning

confidence: 99%

Entropy-Based Approach in Selection Exact String-Matching Algorithms

Markić

Štula

Zoric

et al. 2020

Entropy

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“…We, therefore, propose to introduce a new set of edit operations: 'split' of markup and (in the opposite case) 'join' to deal with such cases. A few studies have examined how we can keep track of changes in markup of data-centric and text-centric XML (Barabucci et al, 2016;Ciancarini et al, 2016), but comparing transcriptions of literary texts on the level of the text as well as the markup presents a number of compelling challenges. A simple example is the presentation of a user's edit operations.…”

Section: Editorial Workflowmentioning

confidence: 99%

Agree to disagree: Modelling co-existing scholarly perspectives on literary text

Bleeker¹,

Buitendijk²,

Dekker³

2019

Digital Scholarship in the Humanities

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This essay addresses two open challenge in the domain of digital scholarly editing: (1) formally defining the meaning of markup, and (2) allowing the reuse and exchange of textual data through a distributed editorial workflow that allows the editing of texts from multiple, diverging yet co-existing perspectives. We argue that successfully addressing these issues would promote the distribution and exchange of scholarly knowledge, on a technical as well as a theoretical level. The essay introduces ongoing work on a new data model for text called ‘TAG’ (Text-as-Graph) and its reference implementation ‘Alexandria’. The essay outlines how TAG, based on a hypergraph for text, can improve the modeling of complex literary texts, and how Alexandria supports the exchange of markup files in a way that sustains scholarly discourse. We discuss three components of TAG: first, the markup technology stack allows for the formal definition of the meaning of markup (‘markup semantics’); secondly, users can add multiple layers of markup that each represent an alternative perspective on text; and finally the editorial workflow is set up in a git-like distributed version management system. As a result, the TAG model provides for the synthesis of dispersed scholarly practices and the advancement of academic discourse.

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“…Despite the outstanding advances in software engineering, existing difference-based approaches still have a number of limitations [40] as they lack the capability of providing efficient two-way comparison between the different revisions. Difference engines may make mistakes by displaying false-negative or false-positive correspondences, as we have previously seen it in this paper and as confirmed in [41] .…”

Section: Evolution Operatorsmentioning

confidence: 99%

A scalable model based approach for data model evolution: Application to space missions data models

Oubelli

Aït‐Ameur

Bedouet

et al. 2018

Computer Languages, Systems & Structures

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible a b s t r a c t During the development of a complex system, data models are the key to a successful engineering process, as they contain and organize all the information manipulated by the different functions involved in the design of the system. Moreover, these data models evolve throughout the design, as the development raises issues that have to be solved through a restructuration of data organization. But any such data model evolution has a deep impact on the functions that have already being defined.Recent research tries to deal with this issue by studying how complex industrial data models evolve from one version to another and how their data instances co-evolve. Complexity and scalability issues make this problem a major scientific challenge, leading to huge gains in development efficiency. This problem is of particular interest in the field of aeronautics and space systems. Indeed, the development of these systems produces many complex data models associated to the designed systems and/or to the systems under design, hence on the one hand data models are available. On the other hand, it is well known that these systems are developed in the context of collaborative projects that may last for decades. In such projects, specifications together with the associated data models are bound to evolve and engineering processes shall take into account this evolution.Our work addresses the problem of data model evolution in a model-driven engineering setting. We focus on minimizing the impact of model evolution on the system development processes in the specific context on the space engineering area, where data models may involve thousands of concepts and relationships, and we investigate the performance of the model-based development (MBD) approach we propose for data model evolution over two space missions, namely PHARAO and MICROSCOPE.

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Measuring the quality of diff algorithms: a formalization

Cited by 8 publications

References 23 publications

Entropy-Based Approach in Selection Exact String-Matching Algorithms

Entropy-Based Approach in Selection Exact String-Matching Algorithms

Agree to disagree: Modelling co-existing scholarly perspectives on literary text

A scalable model based approach for data model evolution: Application to space missions data models

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