Replications, Revisions, and Reanalyses: Managing Variance Theories in Software Engineering
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912159751995392 |
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| author | Frattini, Julian Fischbach, Jannik Fucci, Davide Unterkalmsteiner, Michael Mendez, Daniel |
| author_facet | Frattini, Julian Fischbach, Jannik Fucci, Davide Unterkalmsteiner, Michael Mendez, Daniel |
| contents | Variance theories quantify the variance that one or more independent variables cause in a dependent variable. In software engineering (SE), variance theories are used to quantify -- among others -- the impact of tools, techniques, and other treatments on software development outcomes. To acquire variance theories, evidence from individual empirical studies needs to be synthesized to more generally valid conclusions. However, research synthesis in SE is mostly limited to meta-analysis, which requires homogeneity of the synthesized studies to infer generalizable variance. In this paper, we aim to extend the practice of research synthesis beyond meta-analysis. To this end, we derive a conceptual framework for the evolution of variance theories and demonstrate its use by applying it to an active research field in SE. The resulting framework allows researchers to put new evidence in a clear relation to an existing body of knowledge and systematically expand the scientific frontier of a studied phenomenon. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_12634 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Replications, Revisions, and Reanalyses: Managing Variance Theories in Software Engineering Frattini, Julian Fischbach, Jannik Fucci, Davide Unterkalmsteiner, Michael Mendez, Daniel Software Engineering Variance theories quantify the variance that one or more independent variables cause in a dependent variable. In software engineering (SE), variance theories are used to quantify -- among others -- the impact of tools, techniques, and other treatments on software development outcomes. To acquire variance theories, evidence from individual empirical studies needs to be synthesized to more generally valid conclusions. However, research synthesis in SE is mostly limited to meta-analysis, which requires homogeneity of the synthesized studies to infer generalizable variance. In this paper, we aim to extend the practice of research synthesis beyond meta-analysis. To this end, we derive a conceptual framework for the evolution of variance theories and demonstrate its use by applying it to an active research field in SE. The resulting framework allows researchers to put new evidence in a clear relation to an existing body of knowledge and systematically expand the scientific frontier of a studied phenomenon. |
| title | Replications, Revisions, and Reanalyses: Managing Variance Theories in Software Engineering |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2412.12634 |