IEEE Standard for a Software Quality Metrics Methodology

doi:10.1109/ieeestd.1993.115124

Cited by 12 publications

(6 citation statements)

References 75 publications

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“…A software metric is a function that takes some software data as input and provides a numerical value as an output. The output provides the degree to which the software possesses a certain attribute affecting its quality [84]. To limit the incorrect interpretation of the metric, threshold values are defined.…”

Section: Rq 1 : What Types Of Comments Do Researchers Focus On When A...mentioning

confidence: 99%

Replication package for the paper "A Decade of Code Comment Quality Assessment: A Systematic Literature Review"

2021

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Code comments are important artifacts in software systems and play a paramount role in many software engineering (SE) tasks related to maintenance and program comprehension. However, while it is widely accepted that high quality matters in code comments just as it matters in source code, assessing comment quality in practice is still an open problem. First and foremost, there is no unique definition of quality when it comes to evaluating code comments. The few existing studies on this topic rather focus on specific attributes of quality that can be easily quantified and measured. Existing techniques and corresponding tools may also focus on comments bound to a specific programming language, and may only deal with comments with specific scopes and clear goals (e.g., Javadoc comments at the method level, or in-body comments describing TODOs to be addressed).In this paper, we present a Systematic Literature Review (SLR) of the last decade of research in SE to answer the following research questions: (i) What types of comments do researchers focus on when assessing comment quality? (ii) What quality attributes (QAs) do they consider? (iii) Which tools and techniques do they use to assess comment quality?, and (iv) How do they evaluate their studies on comment quality assessment in general?Our evaluation, based on the analysis of 2353 papers and the actual review of 47 relevant ones, shows that (i) most studies and techniques focus on comments in Java code, thus may not be generalizable to other languages, and (ii) the analyzed studies focus on four main QAs of a total of 21 QAs identified in the literature, with a clear predominance of checking consistency between comments and the code. We observe that researchers rely on manual assessment and specific heuristics rather than the automated assessment of the comment quality attributes, with evaluations often involving surveys of students and the authors of the original studies but rarely professional developers.

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Section: Rq 1 : What Types Of Comments Do Researchers Focus On When A...mentioning

confidence: 99%

Replication package for the paper "A Decade of Code Comment Quality Assessment: A Systematic Literature Review"

2021

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“…One important aspect that is usually neglected in the assessment of the alternatives is human judgment on evaluation of priorities. Obviously, the metric used for assessments are not identical in terms of importance and priority for each application, and the use of metrics and quantitative measures does not stop the need for human judgment [8]. One of the main features of AHP is linking human inputs with the quantitative measurements provided by the metrics [20].…”

Section: B Ranking Deployment Scenariosmentioning

confidence: 99%

“…Thus, the impact of scaling the number of IoT edge nodes producing data is neglected, which might affect the general suitability of a specific deployment.  The measurements of the required metrics do not replace the need for human judgment [8]. Each application has its own requirements which should be specified by the system designer.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Distributed Deep Neural Network Deployment on Edge and Cloud Nodes in IoT Systems

Ashouri

Lorig

Davidsson

et al. 2020

2020 IEEE International Conference on Edge Computing (EDGE)

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For the efficient execution of Deep Neural Networks (DNN) in the Internet of Things, computation tasks can be distributed and deployed on edge nodes. In contrast to deploying all computation to the cloud, the use of Distributed DNN (DDNN) often results in a reduced amount of data that is sent through the network and thus might increase the overall performance of the system. However, finding an appropriate deployment scenario is often a complex task and requires considering several criteria. In this paper, we introduce a multicriteria decision-making method based on the Analytical Hierarchy Process for the comparison and selection of deployment alternatives. We use the RECAP simulation framework to model and simulate DDNN deployments on different scales to provide a comprehensive assessment of deployments to system designers. In a case study, we apply the method to a smart city scenario where different distributions and deployments of a DNN are analyzed and compared.

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“…According to the IEEE standard [7], a software metric is a standard of measure of a degree to which a software system or process possesses some property. There is a wide range of metrics; therefore, it is advised to use a predefined approach to finding the right set of metrics [6].…”

Section: B Metricsmentioning

confidence: 99%

Quality metrics in Agile Software Development Projects

Kārkliņa

Pirta

2018

ITMS

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Nowadays, IT projects are becoming more complex and larger in scale. Stakeholders often experience difficulties assessing project quality attributes, such as progress, budget. Specifically adapted project metrics based on their descriptive features are beneficial tools for acquiring important information. The paper discusses metrics as an important project quality assessment method. It proposes using GQM method for selecting the most appropriate Agile project quality metrics. For metrics monitoring it explores popular cloud-based project management systems. An illustration of the approach is provided by two case studies with Agile projects in the public sector.

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IEEE Standard for a Software Quality Metrics Methodology

Cited by 12 publications

References 75 publications

Replication package for the paper "A Decade of Code Comment Quality Assessment: A Systematic Literature Review"

Replication package for the paper "A Decade of Code Comment Quality Assessment: A Systematic Literature Review"

Analyzing Distributed Deep Neural Network Deployment on Edge and Cloud Nodes in IoT Systems

Quality metrics in Agile Software Development Projects

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