Do software categories impact coupling metrics?

Souza, Lucas Batista Leite de; Maia, Marcelo de Almeida

doi:10.1109/msr.2013.6624030

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…Is there any information about software product quality metrics/measurement management tools in our paper repository? Although there is a lack of integrated quality metric/measurement management tools, which collect and analyze the metrics to measure different quality characteristics, various tools are available to measure different types of quality metrics as given in the following: ASSIST [46], Analizo [47], RAF [48], RTTool [49], iPlasma Tool [50] , MUSE (Muse Understand Scripting Engine) [51], VizzMaintenance [52] and EMERALD (Enhanced Measurement for Early Risk Assessment of Latent Defects) [53]. When we analyzed the name of the software quality datasets and metric tools on the Internet, we encountered the QSM (Quantitative Software Management) tool [54] that supports full measurement processes from beginning to the end for large projects.…”

Section: Rq213mentioning

confidence: 99%

Software Product Quality Metrics: A Systematic Mapping Study

2021

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In the current competitive world, producing quality products has become a prominent factor to succeed in business. In this respect, defining and following the software product quality metrics (SPQM) to detect the current quality situation and continuous improvement of systems have gained tremendous importance. Therefore, it is necessary to review the present studies in this area to allow for the analysis of the situation at hand, as well as to enable us to make predictions regarding the future research areas. The present research aims to analyze the active research areas and trends on this topic appearing in the literature during the last decade. A Systematic Mapping (SM) study was carried out on 70 articles and conference papers published between 2009 and 2019 on SPQM as indicated in their titles and abstract. The result is presented through graphics, explanations, and the mind mapping method. The outputs include the trend map between the years 2009 and 2019, knowledge about this area and measurement tools, issues determined to be open to development in this area, and conformity between conference papers, articles and internationally valid quality models. This study may serve as a foundation for future studies that aim to contribute to the development in this crucial field. Future SM studies might focus on this subject for measuring the quality of network performance and new technologies such as Artificial Intelligence (AI), Internet of things (IoT), Cloud of Things (CoT), Machine Learning, and Robotics. INDEX TERMS software quality, software product quality, metrics, systematic mapping.

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Section: Rq213mentioning

confidence: 99%

Software Product Quality Metrics: A Systematic Mapping Study

2021

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“…Thresholds can aid specialists to apply metrics in their tasks and Medicine is an example of field in which the work of the specialist is strongly supported by metrics and their thresholds. In a different study by De Souza and Maia [66], software coupling metrics were studied based on software categories to identify any impact of software categories on coupling metrics (CBO 4 , DAC 5 , ATFD 6 and AC 7 ). The authors emphasised the need to pay a special attention to software categories when comparing systems in distinct categories with predefined thresholds already available in the literature.…”

Section: Related Workmentioning

confidence: 99%

Detecting Java software similarities by using different clustering techniques

Capiluppi

Ruscio

Rocco

et al. 2020

Information and Software Technology

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Background-Research on empirical software engineering has increasingly been conducted by analysing and measuring vast amounts of software systems. Hundreds, thousands and even millions of systems have been (and are) considered by researchers, and often within the same study, in order to test theories, demonstrate approaches or run prediction models. A much less investigated aspect is whether the collected metrics might be context-specific, or whether systems should be better analysed in clusters. Objective-The objectives of this study are (i) to define a set of clustering techniques that might be used to group similar software systems, and (ii) to evaluate whether a suite of well-known object-oriented metrics is context-specific, and its values differ along the defined clusters. Method-We group software systems based on three different clustering techniques, and we collect the values of the metrics suite in each cluster. We then test whether clusters are statistically different between each other, using the Kolgomorov-Smirnov (KS) hypothesis testing. Results-Our results show that, for two of the used techniques, the KS null hypothesis (e.g., the clusters come from the same population) is rejected for

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“…Table I provides a summary of features and other descriptive information about the projects that were part of our study. We also manually categorized the domain of the projects by looking at the project description and using the categories used by Souza et al [9]. Table II has the summary of the domains of the projects.…”

Section: A Project Selection Criteriamentioning

confidence: 99%

An Empirical Study of Design Degradation: How Software Projects Get Worse over Time

Ahmed

Mannan

Gopinath

et al. 2015

2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Context: Software decay is a key concern for large, long-lived software projects. Systems degrade over time as design and implementation compromises and exceptions pile up.Goal: Quantify design decay and understand how software projects deal with this issue. Method: We conducted an empirical study on the presence and evolution of code smells, used as an indicator of design degradation in 220 open source projects.Results: The best approach to maintain the quality of a project is to spend time reducing both software defects (bugs) and design issues (refactoring). We found that design issues are frequently ignored in favor of fixing defects. We also found that design issues have a higher chance of being fixed in the early stages of a project, and that efforts to correct these stall as projects mature and the code base grows, leading to a build-up of problems.Conclusions: From studying a large set of open source projects, our research suggests that while core contributors tend to fix design issues more often than non-core contributors, there is no difference once the relative quantity of commits is accounted for. We also show that design issues tend to build up over time.

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Do software categories impact coupling metrics?

Cited by 10 publications

References 13 publications

Software Product Quality Metrics: A Systematic Mapping Study

Software Product Quality Metrics: A Systematic Mapping Study

Detecting Java software similarities by using different clustering techniques

An Empirical Study of Design Degradation: How Software Projects Get Worse over Time

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