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Evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called CrashFunction to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MO-HO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to CrashFunction. We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 hard-to-reproduce crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO. We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (SGGA) and decomposition-based multi-objectivization approach (decomposition). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to SGGA and decomposition, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.

Software testing is an important and time-consuming task that is often done manually. In the last decades, researchers have come up with techniques to generate input data (e.g., fuzzing) and automate the process of generating test cases (e.g., search-based testing). However, these techniques are known to have their own limitations: search-based testing does not generate highly-structured data; grammar-based fuzzing does not generate test case structures. To address these limitations, we combine these two techniques. By applying grammar-based mutations to the input data gathered by the search-based testing algorithm, it allows us to co-evolve both aspects of test case generation. We evaluate our approach by performing an empirical study on 20 Java classes from the three most popular JSON parsers across multiple search budgets. Our results show that the proposed approach on average improves branch coverage for JSON related classes by 15% (with a maximum increase of 50%) without negatively impacting other classes.

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 cases. In this paper, we introduce Botsing, an open-source search-based crash reproduction framework for Java. Botsing implements state-of-the-art and novel approaches for crash reproduction. The well-documented architecture of Botsing makes it an easy-to-extend framework, and can hence be used for implementing new approaches to improve crash reproduction. We have applied Botsing to a wide range of crashes collected from open source systems. Furthermore, we conducted a qualitative assessment of the crash-reproducing test cases with our industrial partners. In both cases, Botsing could reproduce a notable amount of the given stack traces.

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 reproduce a limited number of crashes due to a lack of diversity in the population during the search. In this study, we address this issue by applying Multi-Objectivization using Helper-Objectives (MO-HO) on crash reproduction. In particular, we add two helper-objectives to the Crash Distance to improve the diversity of the generated test cases and consequently enhance the guidance of the search process. We assessed MO-HO against the single-objective crash reproduction. Our results show that MO-HO can reproduce two additional crashes that were not previously reproducible by the single-objective approach.

EvoSuite is a search-based tool that automatically generates executable unit tests for Java code (JUnit tests). This paper summarizes the results and experiences of EvoSuite’s participation at the eighth unit testing competition at SBST 2020, where EvoSuite achieved the highest overall score (406.14 points) for the seventh time in eight editions of the competition.

The rise in popularity of machine learning (ML), and deep learning in particular, has both led to optimism about achievements of artificial intelligence, as well as concerns about possible weaknesses and vulnerabilities of ML pipelines. Within the software engineering community, this has led to a considerable body of work on ML testing techniques, including white- and black-box testing for ML models. This means the oracle problem needs to be addressed; for supervised ML applications, oracle information is indeed available in the form of dataset “ground truth”, that encodes input data with corresponding desired output labels. However, while ground truth forms a gold standard, there still is no guarantee it is truly correct. Indeed, syntactic, semantic, and conceptual framing issues in the oracle may negatively affect the ML system integrity. While syntactic issues may be automatically verified and corrected, the higher-level issues traditionally need human judgment and manual analysis. In this paper, we employ two heuristics based on information entropy and semantic analysis on well-known computer vision models and benchmark data from ImageNet. The heuristics are used to semi-automatically uncover potential higher-level issues in (i) the label taxonomy used to define the ground truth oracle (labels), and (ii) data encoding and representation. In doing this, beyond existing ML testing efforts, we illustrate the need for SE strategies that especially target and assess the oracle.

Build logs are textual by-products that a software build process creates, often as part of its Continuous Integration (CI) pipeline. Build logs are a paramount source of information for developers when debugging into and understanding a build failure. Recently, attempts to partly automate this time-consuming, purely manual activity have come up, such as rule- or information-retrieval-based techniques. We believe that having a common data set to compare different build log analysis techniques will advance the research area. It will ultimately increase our understanding of CI build failures. In this paper, we present LogChunks, a collection of 797 annotated Travis CI build logs from 80 GitHub repositories in 29 programming languages. For each build log, LogChunks contains a manually labeled log part (chunk) describing why the build failed. We externally validated the data set with the developers who caused the original build failure. The width and depth of the LogChunks data set are intended to make it the default benchmark for automated build log analysis techniques

Abstract : Application Programming Interfaces (APIs) typically come with (implicit) usage constraints. The violations of these constraints (API misuses) can lead to software crashes. Even though there are several tools that can detect API misuses, most of them suffer from a very high rate of false positives.

Abstract Latent Dirichlet Allocation (LDA) has been used to support many software engineering tasks. Previous studies showed that default settings lead to sub-optimal topic modeling with a dramatic impact on the performance of such approaches in terms of precision and recall.