Dataset of the research paper: Machine Learning for Software Engineering: A Tertiary Study Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 39 reviews on ML for SE published between 2009–2020, covering 2,506 primary studies. The SE areas most tackled with ML are software testing and quality, 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. review-protocol.md: The protocol employed in this tertiary study data/ dl-search/ input/ acm_comput_surveys_overviews.bib: Surveys of ACM Computing Surveys journal acm_comput_surveys_overviews_titles.txt: Titles of surveys acm_comput_ml_surveys.bib: ML-related surveys of ACM Computing Surveys journal acm_comput_ml_surveys_titles.txt: Titles of ML-related surveys dl_search_queries.txt: Search queries applied to IEEE Xplore, ACM Digital Library, and Elsevier Scopus ml_keywords.txt: ML-related keywords extracted from ML-related survey titles and used in the search queries se_keywords.txt: 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 secondary_studies_keywords.txt: Survey-related keywords composed of the 15 keywords introduced in the tertiary study on SLRs in SE by Kitchenham et al. (2010), and the survey titles, and used in the search queries output/ acm/ acm{1–9}.bib: Search results from ACM Digital Library ieee.csv: Search results from IEEE Xplore scopus_analyze_year.csv: Yearly distribution of ML and SE documents extracted from Scopus's Analyze search results page scopus.csv: Search results from Scopus study-selection/ backward_snowballing.csv: Additional secondary studies found through the backward snowballing process cohen_kappa_agreement.csv: Inter-rater reliability of reviewers in study selection dl_search_results.csv: Aggregated search results of all three digital libraries study_selection_reviewer_{1–2}.csv: Divided search results assessed by reviewer 1 and 2, correspondingly, based on IC/EC quality-assessment/ dare_assessment.csv: 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 quality_accepted_studies.csv: Details of quality-accepted studies studies_for_review.bib: Bibliography details and QA scores of selected secondary studies data-extraction/ further_research.csv: Recommendations for further research of quality-accepted studies knowledge_areas.csv: Classification of quality-accepted studies using the SWEBOK KAs and subareas ml_techniques.csv: Classification of the quality-accepted studies based on a four-axis ML classification scheme, along with extracted ML techniques employed in the studies primary_studies.csv: Details of reviewed primary studies by the quality-accepted secondary research_methods.csv: Citations of the research methods employed by the quality-accepted studies research_types_methods.csv: Research types and methods employed by the quality-accepted studies src/ data-analysis.ipynb: Analysis of data extraction results (data preprocessing, top authors and institutions, study types, yearly distribution of publishers and QA scores) and creation of all figures included in the study scopus-year-analysis.ipynb: Yearly distribution of ML and SE publications retrieved from Elsevier Scopus study-selection-preprocessing.ipynb: Processing of digital library search results to conduct the inter-rater reliability estimation and study selection process