Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments

Testing with simulation environments helps to identify critical failing scenarios for self-driving cars (SDCs). Simulation-based tests are safer than in-field operational tests and allow detecting software defects before deployment. However, these tests are very expensive and are too many to be run frequently within limited time constraints...

Generating Class-Level Integration Tests Using Call Site Information

Search-based approaches have been used in the literature to automate the process of creating unit test cases. However, related work has shown that generated tests with high code coverage could be ineffective, i.e., they may not detect all faults or kill all injected mutants. In this paper, we propose Cling, an integration-level test case...

Basic Block Coverage for Unit Test Generation at the SBST 2022 Tool Competition

Basic Block Coverage (BBC) is a secondary objective for search-based unit test generation techniques relying on the approach level and branch distance to drive the search process. Unlike the approach level and branch distance, which considers only information related to the coverage of explicit branches coming from conditional and loop...

Basic block coverage for search-based unit testing and crash reproduction

Search-based techniques have been widely used for white-box test generation. Many of these approaches rely on the approach level and branch distance heuristics to guide the search process and generate test cases with high line and branch coverage. Despite the positive results achieved by these two heuristics, they only use the information...

Carving Information Sources to Drive Search-Based Crash Reproduction and Test Case Generation

Software testing is one of the essential and expensive tasks in software development. Hence, many approaches were introduced to automate different software testing tasks. Among these techniques, search-based test generation techniques have been vastly applied in real-world cases and have shown promising results. These strategies apply search...

Hybrid Multi-level Crossover for Unit Test Case Generation

State-of-the-art search-based approaches for test case generation work at test case level, where tests are represented as sequences of statements. These approaches make use of genetic operators (i.e., mutation and crossover) that create test variants by adding, altering, and removing statements from existing tests. While this encoding schema has...

Summary of Search-based Crash Reproduction using Behavioral Model Seeding

This is an extended abstract of the article: Pouria Derakhshanfar, Xavier Devroey, Gilles Perrouin, Andy Zaidman and Arie van Deursen. 2019. Search-based crash reproduction using behavioural model seeding. In: Software Testing, Verification and Reliability (May 2020). http://doi.org/10.1002/stvr.1733.

An Application of Model Seeding to Search-based Unit Test Generation for Gson

Model seeding is a strategy for injecting additional information in a search-based test generation process in the form of models, representing usages of the classes of the software under test. These models are used during the search-process to generate logical sequences of calls whenever an instance of a specific class is required. Model seeding...

It is not Only About Control Dependent Nodes: Basic Block Coverage for Search-Based Crash Reproduction

Search-based techniques have been widely used for white-box test generation. Many of these approaches rely on the approach level and branch distance heuristics to guide the search process and generate test cases with high line and branch coverage. Despite the positive results achieved by these two heuristics, they only use the information...

Commonality-Driven Unit Test Generation

Various search-based test generation techniques have been proposed to automate the generation of unit tests fulfilling different criteria (e.g., line coverage, branch coverage, mutation score, etc.). Despite several advances made over the years, search-based unit test generation still suffers from a lack of guidance due to the limited amount of...

A benchmark-based evaluation of search-based crash reproduction

Crash reproduction approaches help developers during debugging by generating a test case that reproduces a given crash. Several solutions have been proposed to automate this task. However, the proposed solutions have been evaluated on a limited number of projects, making comparison difficult. In this paper, we enhance this line of research by...

Crash Reproduction Using Helper Objectives

Evolutionary-based crash reproduction techniques aid developers in their debugging practices by generating a test case that reproduces a crash given its stack trace. In these techniques, the search process is typically guided by a single search objective called Crash Distance. Previous studies have shown that current approaches could only...

Search-based crash reproduction using behavioural model seeding

Search-based crash reproduction approaches assist developers during debugging by generating a test case, which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the...

Botsing, a Search-based Crash Reproduction Framework for Java

Approaches for automatic crash reproduction aim to generate test cases that reproduce crashes starting from the crash stack traces. These tests help developers during their debugging practices. One of the most promising techniques in this research field leverages search-based software testing techniques for generating crash reproducing test...

Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives

Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence, evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test...