Machine Learning for Software Engineering: A Tertiary Study

Dataset of the research paper: <strong>Machine Learning for Software Engineering: A Tertiary Study</strong> Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches. The following data and source files are included. <strong>review-protocol.md</strong>: The protocol employed in this tertiary study <strong>data/</strong> <strong> dl-search/</strong> <strong> input/</strong> <strong>acm_comput_surveys_overviews.bib</strong>: Surveys of ACM Computing Surveys journal <strong>acm_comput_surveys_overviews_titles.txt</strong>: Titles of surveys <strong>acm_comput_ml_surveys.bib</strong>: Machine learning (ML)-related surveys of ACM Computing Surveys journal <strong>acm_comput_ml_surveys_titles.txt</strong>: Titles of ML-related surveys <strong>dl_search_queries.txt</strong>: Search queries applied to IEEE Xplore, ACM Digital Library, and Elsevier Scopus <strong>ml_keywords.txt</strong>: ML-related keywords extracted from ML-related survey titles and used in the search queries <strong>se_keywords.txt</strong>: Software Engineering (SE)-related keywords derived from the 15 SWEBOK Knowledge Areas (KAs—except for Computing Foundations, Mathematical Foundations, and Engineering Foundations) and used in the search queries <strong>secondary_studies_keywords.txt</strong>: Survey-related keywords composed of the 15 keywords introduced in the tertiary study on SLRs in SE by Kitchenham <em>et al.</em> (2010), and the survey titles, and used in the search queries <strong> output/</strong> <strong>acm/</strong> <strong>acm{1–9}.bib</strong>: Search results from ACM Digital Library <strong>ieee.csv</strong>: Search results from IEEE Xplore <strong>scopus_analyze_year.csv</strong>: Yearly distribution of ML and SE documents extracted from Scopus's <em>Analyze search results</em> page <strong>scopus.csv</strong>: Search results from Scopus <strong> study-selection/</strong> <strong>backward_snowballing.csv</strong>: Additional secondary studies found through the backward snowballing process <strong>backward_snowballing_references.csv</strong>: References of quality-accepted secondary studies <strong>cohen_kappa_agreement.csv</strong>: Inter-rater reliability of reviewers in study selection <strong>dl_search_results.csv</strong>: Aggregated search results of all three digital libraries <strong>forward_snowballing_reviewer_{1,2}.csv</strong>: Divided forward snowballing citations of quality-accepted studies assessed by reviewer 1 and 2, correspondingly, based on IC/EC <strong>study_selection_reviewer_{1,2}.csv</strong>: Divided search results assessed by reviewer 1 and 2, correspondingly, based on IC/EC <strong> quality-assessment/</strong> <strong>dare_assessment.csv</strong>: Quality assessment (QA) of selected secondary studies based on the Database of Abstracts of Reviews of Effects (DARE) criteria by York University, Centre for Reviews and Dissemination <strong>quality_accepted_studies.csv</strong>: Details of quality-accepted studies <strong>studies_for_review.bib</strong>: Bibliography details and QA scores of selected secondary studies <strong> data-extraction/</strong> <strong>further_research.csv</strong>: Recommendations for further research of quality-accepted studies <strong>further_research_general.csv</strong>: The complete list of associated studies for each general recommendation <strong>knowledge_areas.csv</strong>: Classification of quality-accepted studies using the SWEBOK KAs and subareas <strong>ml_techniques.csv</strong>: Classification of the quality-accepted studies based on a four-axis ML classification scheme, along with extracted ML techniques employed in the studies <strong>primary_studies.csv</strong>: Details of reviewed primary studies by the quality-accepted secondary <strong>research_methods.csv</strong>: Citations of the research methods employed by the quality-accepted studies <strong>research_types_methods.csv</strong>: Research types and methods employed by the quality-accepted studies <strong>src/</strong> <strong>data-analysis.ipynb</strong>: Analysis of data extraction results (data preprocessing, top authors and institutions, study types, yearly distribution of publishers, QA scores, and SWEBOK KAs) and creation of all figures included in the study <strong>scopus-year-analysis.ipynb</strong>: Yearly distribution of ML and SE publications retrieved from Elsevier Scopus <strong>study-selection-preprocessing.ipynb</strong>: Processing of digital library search results to conduct the inter-rater reliability estimation and study selection process