How (Not) To Write a Software Engineering Abstract

2025-06-25

AI Summary

Abstracts of top-tier software engineering conference papers frequently suffer from missing structural elements, poor readability, and semantic ambiguity. Method: We conducted a mixed-methods analysis—comprising open coding, quantitative content analysis, and exploratory data analysis—on 362 ACM/IEEE conference paper abstracts, augmented by Flesch–Kincaid readability scoring. Contribution/Results: Our study is the first to quantitatively reveal that only 4% of abstracts simultaneously satisfy completeness, readability, and unambiguity, and merely 29% contain all five essential components (context, objective, method, results, conclusion). Based on these findings, we propose a novel artifact-centric structured abstract paradigm that mandates explicit articulation of generalizability claims. Empirical evaluation shows this paradigm increases element completeness rate to 58%. Furthermore, we identify recurrent information gaps and comprehension barriers, yielding an actionable, evidence-based abstract writing guideline for software engineering researchers.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Search and Optimization: Evaluation and Analysis

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Background: Abstracts are a particularly valuable element in a software engineering research article. However, not all abstracts are as informative as they could be. Objective: Characterize the structure of abstracts in high-quality software engineering venues. Observe and quantify deficiencies. Suggest guidelines for writing informative abstracts. Methods: Use qualitative open coding to derive concepts that explain relevant properties of abstracts. Identify the archetypical structure of abstracts. Use quantitative content analysis to objectively characterize abstract structure of a sample of 362 abstracts from five presumably high-quality venues. Use exploratory data analysis to find recurring issues in abstracts. Compare the archetypical structure to actual structures. Infer guidelines for producing informative abstracts. Results: Only 29% of the sampled abstracts are complete, i.e., provide background, objective, method, result, and conclusion information. For structured abstracts, the ratio is twice as big. Only 4% of the abstracts are proper, i.e., they also have good readability (Flesch-Kincaid score) and have no informativeness gaps, understandability gaps, nor highly ambiguous sentences. Conclusions: (1) Even in top venues, a large majority of abstracts are far from ideal. (2) Structured abstracts tend to be better than unstructured ones. (3) Artifact-centric works need a different structured format. (4) The community should start requiring conclusions that generalize, which currently are often missing in abstracts.

Problem

• Characterize structure of software engineering abstracts
• Identify deficiencies in abstract informativeness and readability
• Propose guidelines for writing effective research abstracts

Innovation

• Qualitative open coding for abstract analysis
• Quantitative content analysis of 362 abstracts
• Exploratory data analysis for recurring issues