| 1. |
- Ericsson, Lovisa, et al.
(författare)
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Revisiting socio-economic inequalities in sedentary leisure time in Sweden : An intersectional analysis of individual heterogeneity and discriminatory accuracy (AIHDA)
- 2023
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Ingår i: Scandinavian Journal of Public Health. - : Sage Publications. - 1403-4948 .- 1651-1905. ; 51:4, s. 570-578
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Tidskriftsartikel (refereegranskat)abstract
- Aims: Swedish public health reports have repeatedly provided information about socio-economic inequalities in sedentary leisure time, despite that, in the interest of health equity, physical activity should be equally distributed in the population. Such public health reports, however, neither consider the intersection of multiple socio-demographic factors nor the individual heterogeneity around group averages. Drawing on intersectionality theory, this study aimed to revisit previous findings on sedentary leisure time from Swedish public health surveys and demonstrate how the analysis of individual heterogeneity and discriminatory accuracy (AIHDA) can be used for analysing complex health inequalities.Methods: Using data from Swedish national public health surveys (2004–2015), we applied the AIHDA to define 72 intersectional groups by categories of age, gender, educational achievement, migration status and household composition. We then calculated (a) the absolute and relative risk of sedentary leisure time and (b) the discriminatory accuracy (DA) of the intersectional grouping.Results: The average risk of sedentary leisure time ranged from 5.8% among native-born, highly educated, young women living alone to 41.0% among immigrated young men, living alone, with low education. The risk was higher in strata comprising immigrated people with low education and lower in strata including native-born, highly educated people. However, the DA of the grouping was poor, indicating a substantial overlap of individual risk between groups.Conclusions: Using the AIHDA and drawing on intersectionality, this study provides an improved mapping of the socio-economic distribution of sedentary leisure time in Sweden, with the poor DA suggesting universal rather than targeted physical activity interventions.
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| 2. |
- Ulan, Maria, et al.
(författare)
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Aggregation as Unsupervised Learning and its Evaluation
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Regression uses supervised machine learning to find a model that combines several independent variables to predict a dependent variable based on ground truth (labeled) data, i.e., tuples of independent and dependent variables (labels). Similarly, aggregation also combines several independent variables to a dependent variable. The dependent variable should preserve properties of the independent variables, e.g., the ranking or relative distance of the independent variable tuples, and/or represent a latent ground truth that is a function of these independent variables. However, ground truth data is not available for finding the aggregation model. Consequently, aggregation models are data agnostic or can only be derived with unsupervised machine learning approaches.We introduce a novel unsupervised aggregation approach based on intrinsic properties of unlabeled training data, such as the cumulative probability distributions of the single independent variables and their mutual dependencies.For assessing this against other aggregation approaches, two perspectives are relevant: (i) how well the aggregation output represents properties of the input tuples, and (ii) how well can aggregated output predict a latent ground truth. We present an empirical evaluation framework that allows to evaluate aggregation approaches from both perspectives. To this end, we use data sets for assessing supervised regression approaches that contain explicit ground truth labels. However, the ground truth is not used for deriving the aggregation models, but it allows for the assessment from a perspective (ii). More specifically, we use regression data sets from the UCI machine learning repository and benchmark several data-agnostic and unsupervised approaches for aggregation against ours.The benchmark results indicate that our approach outperforms the other data-agnostic and unsupervised aggregation approaches. It is almost on par with linear regression.
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| 3. |
- Ulan, Maria, et al.
(författare)
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Artifact: Quality Models Inside Out : Interactive Visualization of Software Metrics by Means of Joint Probabilities
- 2018
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Annan publikation (mjukvara/multimedium) (refereegranskat)abstract
- Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.
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| 4. |
- Ulan, Maria, et al.
(författare)
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Copula-based software metrics aggregation
- 2021
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Ingår i: Software quality journal. - : Springer. - 0963-9314 .- 1573-1367. ; 29, s. 863-899
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Tidskriftsartikel (refereegranskat)abstract
- A quality model is a conceptual decomposition of an abstract notion of quality into relevant, possibly conflicting characteristics and further into measurable metrics. For quality assessment and decision making, metrics values are aggregated to characteristics and ultimately to quality scores. Aggregation has often been problematic as quality models do not provide the semantics of aggregation. This makes it hard to formally reason about metrics, characteristics, and quality. We argue that aggregation needs to be interpretable and mathematically well defined in order to assess, to compare, and to improve quality. To address this challenge, we propose a probabilistic approach to aggregation and define quality scores based on joint distributions of absolute metrics values. To evaluate the proposed approach and its implementation under realistic conditions, we conduct empirical studies on bug prediction of ca. 5000 software classes, maintainability of ca. 15000 open-source software systems, and on the information quality of ca. 100000 real-world technical documents. We found that our approach is feasible, accurate, and scalable in performance.
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| 5. |
- Ulan, Maria, et al.
(författare)
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Introducing Quality Models Based On Joint Probabilities : Introducing Quality Models Based On Joint Probabilities
- 2018
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Ingår i: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings. - New York, NY, USA : IEEE. - 9781450356633 ; , s. 216-217
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Konferensbidrag (refereegranskat)abstract
- Multi-dimensional goals can be formalized in so-called quality models. Often, each dimension is assessed with a set of metrics that are not comparable; they come with different units, scale types, and distributions of values. Aggregating the metrics to a single quality score in an ad-hoc manner cannot be expected to provide a reliable basis for decision making. Therefore, aggregation needs to be mathematically well-defined and interpretable. We present such a way of defining quality models based on joint probabilities. We exemplify our approach using a quality model with 30 standard metrics assessing technical documentation quality and study ca. 20,000 real-world files. We study the effect of several tests on the independence and results show that metrics are, in general, not independent. Finally, we exemplify our suggested definition of quality models in this domain.
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| 6. |
- Ulan, Maria, et al.
(författare)
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Multi-criteria Ranking Based on Joint Distributions : A Tool to Support Decision Making
- 2019
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Ingår i: Perspectives in Business Informatics Research.BIR 2019. - Cham : Springer. - 9783030311421 - 9783030311438 ; , s. 74-88
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Konferensbidrag (refereegranskat)abstract
- Sound assessment and ranking of alternatives are fundamental to effective decision making. Creating an overall ranking is not trivial if there are multiple criteria, and none of the alternatives is the best according to all criteria. To address this challenge, we propose an approach that aggregates criteria scores based on their joint (probability) distribution and obtains the ranking as a weighted product of these scores. We evaluate our approach in a real-world use case based on a funding allocation problem and compare it with the traditional weighted sum aggregation model. The results show that the approaches assign similar ranks, while our approach is more interpretable and sensitive.
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| 7. |
- Ulan, Maria, et al.
(författare)
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Quality Models Inside Out : Interactive Visualization of Software Metrics by Means of Joint Probabilities
- 2018
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Ingår i: Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018. - : IEEE. - 9781538682920 - 9781538682937 ; , s. 65-75
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Konferensbidrag (refereegranskat)abstract
- Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.
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| 8. |
- Ulan, Maria, et al.
(författare)
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Towards Meaningful Software Metrics Aggregation
- 2020
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Ingår i: Proceedings of the 18th Belgium- Netherlands Software Evolution Workshop. - : CEUR-WS.org.
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Konferensbidrag (refereegranskat)abstract
- Aggregation of software metrics is a challenging task, it is even more complex when it comes to considering weights to indicate the relative importance of software metrics. These weights are mostly determined manually, it results in subjective quality models, which are hard to interpret. To address this challenge, we propose an automated aggregation approach based on the joint distribution of software metrics. To evaluate the effectiveness of our approach, we conduct an empirical study on maintainability assessment for around 5000 classes from open source software systems written in Java and compare our approach with a classical weighted linear combination approach in the context of maintainability scoring and anomaly detection. The results show that approaches assign similar scores, while our approach is more interpretable, sensitive, and actionable.
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| 9. |
- Ulan, Maria, et al.
(författare)
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Weighted software metrics aggregation and its application to defect prediction
- 2021
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Ingår i: Empirical Software Engineering. - : Springer. - 1382-3256 .- 1573-7616. ; 26:5
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Tidskriftsartikel (refereegranskat)abstract
- It is a well-known practice in software engineering to aggregate software metrics to assess software artifacts for various purposes, such as their maintainability or their proneness to contain bugs. For different purposes, different metrics might be relevant. However, weighting these software metrics according to their contribution to the respective purpose is a challenging task. Manual approaches based on experts do not scale with the number of metrics. Also, experts get confused if the metrics are not independent, which is rarely the case. Automated approaches based on supervised learning require reliable and generalizable training data, a ground truth, which is rarely available. We propose an automated approach to weighted metrics aggregation that is based on unsupervised learning. It sets metrics scores and their weights based on probability theory and aggregates them. To evaluate the effectiveness, we conducted two empirical studies on defect prediction, one on ca. 200 000 code changes, and another ca. 5 000 software classes. The results show that our approach can be used as an agnostic unsupervised predictor in the absence of a ground truth.
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