Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone -- A Systematic Comparison of Search Algorithms for Topic Modelling -- A Study on Duplicate Bug Report Identification -- Constructing Search Spaces for SBST using Machine Learning -- A Review of Ten Years of the Symposium on Search-Based Software Engineering -- Does Diversity Improve the Test Suite Generation for Mobile Applications -- Detect Performance Regression by Combining Static and Dynamic metrics Using Evolutionary Algorithms -- General Program Synthesis using Guided Corpus Generation and Automatic Refactoring -- A Search-Based Approach to Generate MC/DC Test Data for OCL Constraints -- Bio-Inspired Optimization of Test Data Generation for Concurrent Programs -- Revisiting Hyper-Parameter Tuning for Search-based Software Testing -- Towards Automated Boundary Value Testing with Program Derivatives and Search -- Code Naturalness to Assist Search Space Exploration in Search-based Program Repair Methods -- Dorylus:An Ant Colony Based Tool for Automated Test Case Generation -- Software Improvement with Gin.

This book constitutes the refereed proceedings of the 11th International Symposium on Search-Based Software Engineering, SSBSE 2019, held in Tallinn, Estonia, in August/September 2019. The 9 research papers and 3 short papers presented together with 1 keynote and 1 challenge paper were carefully reviewed and selected from 28 submissions. SSBSE is a research area focused on the formulation of software engineering problems as search problems, and the subsequent use of complex heuristic techniques to attain optimal solutions to such problems. A wealth of engineering challenges - from test generation, to design refactoring, to process organization - can be solved efficiently through the application of automated optimization techniques. SBSE is a growing field - sitting at the crossroads between AI, machine learning, and software engineering - and SBSE techniques have begun to attain human-competitive results.