dataset stratification

This work addresses the frequent neglect of sampling strategy design and generalizability in software engineering research, which often undermines the representativeness of empirical findings. To remedy this, the paper introduces a domain-specific language (DSL) that explicitly models complex sampling workflows over code repositories through composable sampling operators, enabling—for the first time—formal specification and reasoning about the generalizability of sampling strategies. Implemented as a fluent Python API, the DSL is integrated with a statistical metric system to quantitatively assess the external validity of sampled datasets. The authors demonstrate the expressiveness and practical utility of their approach by reconstructing and formalizing the sampling procedures from multiple Mining Software Repositories (MSR) studies, thereby validating the framework’s capacity to capture real-world methodological diversity.

This study addresses the lack of rigorous statistical assessment for the reliability of output structures in complex clustering pipelines that involve multiple data-dependent stages such as anomaly detection, feature selection, and clustering. To bridge this gap, the work systematically applies selective inference to the entire clustering analysis workflow, establishing a statistical framework that enables valid significance testing of final cluster assignments. The proposed method rigorously controls the type I error rate at any pre-specified nominal level and demonstrates strong empirical performance on both synthetic and real-world datasets. By doing so, it provides a principled and reliable foundation for statistical inference in multi-stage, data-driven clustering procedures.

Implementation discrepancies across software repository mining tools severely threaten the validity of empirical findings. Method: We conduct a dual-tool comparative analysis of 10 large-scale open-source projects, systematically identifying how minor implementation differences—such as commit parsing logic and author deduplication rules—induce up to 500% deviation in key metrics (e.g., commit count, developer count). We propose a “tool-level configuration + post-hoc normalization” co-optimization framework to mitigate metric divergence and perform multi-tool experiments, quantitative consistency assessment, and code-level root-cause analysis. Contribution/Results: We identify six technical sources undermining data validity and establish the first validity assessment paradigm for Mining Software Projects Research (MSPR) explicitly addressing tool heterogeneity—thereby enabling rigorous, reproducible, and comparable empirical software engineering studies.

This study addresses a critical limitation in traditional reproducible research, where sharing only code and results fails to expose the implicit assumptions, expectations, and premises underlying an analyst’s reasoning—thereby hindering thorough evaluation of analytical quality. To overcome this, the paper proposes a formal modeling framework that explicitly translates the analyst’s tacit reasoning process into structured logical representations, statically capturing the construction logic of the analysis. This approach enables systematic scrutiny of the analytical chain of reasoning, assumption sensitivity, and conclusion robustness—even in the absence of the original data. Empirical validation on representative data analysis tasks demonstrates the framework’s effectiveness, achieving both logical visualization and data-free static assessment of analytical integrity.

This work addresses the limitations of the Category-Partition (CP) testing method, which is often hindered by tedious manual execution and error-prone processes due to a lack of automation and visualization support. To overcome these challenges, the authors design and implement a CP testing tool featuring an integrated graphical user interface that fully automates the entire workflow—from defining parameters, environment variables, categories, and options (including constraints) to constructing test frames and generating test cases. The tool introduces type-aware option specifications (supporting Boolean, integer, real, and string types), a robust constraint-handling mechanism, and multiple combinatorial generation strategies, significantly enhancing both expressiveness and usability. Empirical validation through nine case studies demonstrates that the tool efficiently produces valid CP-compliant test cases, effectively supporting systematic test design.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

This work addresses the significant limitations of spreadsheet-based analysis in reproducibility, auditability, version control, and automation. It proposes a migration pathway from Excel to research-grade analytical workflows by leveraging Python’s pandas library as a bridge. The study introduces an innovative set of Excel-to-pandas mapping rules, categorizes nine canonical workflow patterns, and compiles a catalog of common failure modes. Seven end-to-end real-world examples demonstrate the approach in practice. By retaining Excel as a familiar interface for input and output while integrating version control, automated refreshing, and seamless incorporation of statistical and machine learning methods, the proposed framework enables governed, reproducible, and auditable tabular data analysis.

This study addresses the prevalent ad hoc and non-standardized practices in model integration and deployment within MLOps projects, which often stem from a lack of systematic architectural guidance. To bridge this gap, the authors conduct a gray literature review of 103 online sources and apply thematic analysis to derive, for the first time, 25 architecturally significant best practices. These practices are systematically categorized into five thematic groups, with explicit articulation of each practice’s impact on overall system architecture. The resulting framework offers a structured, actionable set of guidelines for MLOps model integration and deployment, providing both researchers and engineering teams with a coherent theoretical foundation and practical reference for designing robust, scalable machine learning systems.