A DSL for specifying timing requirements

Göknil, Arda; Peraldi-Frati, Marie-Agnès

doi:10.1109/modre.2012.6360074

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…It permits to specify QoS attributes e.g., response time or availability, for individual services. Lastly, Goknil and Peraldi-Frati developed a DSL that provides dedicated language elements for expressing time spans, jitter, minimum, and maximum of measures [10].…”

Section: Related Workmentioning

confidence: 99%

A DSL for Defining Feature-Level Quality Constraints and the Aggregation of Evaluation Results in DevOps

Haindl¹,

Plösch²

2022

Preprint

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Quality requirements typically differ among software features, e.g., due to different usage contexts of the features, different impacts of related quality deficiencies onto overall user satisfaction, or long-term plans of the developing organization. For instance, maintainability requirements might be particularly high for software features which are frequently used or bear strategic value for the developing organization. Also, software features where even the smallest delays are perceived as negative by the user will be subjected to specially tight performance requirements. We defined an operational DSL to define software quality requirements as individual feature-level constraints based on quantitative measures. The DSL provides language elements to define the operationalization of measures from external systems, time series operations, time filters, and the automatic evaluation of these feature-level constraints in DevOps based on comparison operators and threshold values. In addition, quality ratings summarize evaluation results of features on an ordinal grading scheme. Likewise, quality gates use these quality ratings to reflect the fitness of software features or the overall software product using different states. Finally, we show an example based on a widely-adopted secure mobile messaging app that illustrates the interplay of the different DSL elements.

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Section: Related Workmentioning

confidence: 99%

A DSL for Defining Feature-Level Quality Constraints and the Aggregation of Evaluation Results in DevOps

Haindl¹,

Plösch²

2022

Preprint

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“…To alleviate the aforementioned restrictions, we proposed a meta-model based approach to facilitate the modeling, visualization, and prioritization of requirements and their related test cases [21]. The proposed meta-model borrows concepts from System Modeling Language (SysML 1 ) which can be found in other meta-models in the literature (for instance representing stakeholder information [22], requirements [17] and their relationships [17] [23]). The proposed meta-model is capable of modeling requirements along with interdependencies between them and other factors (e.g., risk, cost, time to develop and business value) that are significant for RP.…”

Section: Introductionmentioning

confidence: 99%

MBRP: Model-Based Requirements Prioritization Using PageRank Algorithm

Abbas

Inayat

Jan

et al. 2019

2019 26th Asia-Pacific Software Engineering Conference (APSEC)

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Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm's efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.

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