Recently many mutation testing tools have been proposed that rely on bug-fix patterns and natural language models trained on large code corpus. As these tools operate fundamentally differently from the grammar-based traditional approaches, a question arises of how these tools compare in terms of 1) fault detection and 2) cost-effectiveness. Simultaneously, mutation testing research proposes mutant selection approaches based on machine learning to mitigate its application cost. This raises another question: How do the existing mutation testing tools compare when guided by mutant selection approaches? To answer these questions, we compare four existing tools – μBERT (uses pre-trained language model for fault seeding), IBIR (relies on inverted fix-patterns), DeepMutation (generates mutants by employing Neural Machine Translation) and PIT (applies standard grammar-based rules) in terms of fault detection capability and cost-effectiveness, in conjunction with standard and deep learning based mutant selection strategies. Our results show that IBIR has the highest fault detection capability among the four tools; however, it is not the most cost-effective when considering different selection strategies. On the other hand, μBERT having a relatively lower fault detection capability, is the most cost-effective among the four tools. Our results also indicate that comparing mutation testing tools when using deep learning-based mutant selection strategies can lead to different conclusions than the standard mutant selection. For instance, our results demonstrate that combining μBERT with deep learning-based mutant selection yields 12% higher fault detection than the considered tools.

Much research on software engineering relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-world software faults since it “blindly” injects large numbers of faults. It remains indeed challenging to inject few but realistic faults that target a particular functionality in a program. In this work, we introduce iBiR , a fault injection tool that addresses this challenge by exploring change patterns associated to user-reported faults. To inject realistic faults, we create mutants by re-targeting a bug-report-driven automated program repair system, i.e., reversing its code transformation templates. iBiR is further appealing in practice since it requires deep knowledge of neither code nor tests, just of the program’s relevant bug reports. Thus, our approach focuses the fault injection on the feature targeted by the bug report. We assess iBiR by considering the Defects4J dataset. Experimental results show that our approach outperforms the fault injection performed by traditional mutation testing in terms of semantic similarity with the original bug, when applied at either system or class levels of granularity, and provides better, statistically significant estimations of test effectiveness (fault detection). Additionally, when injecting 100 faults, iBiR injects faults that couple with the real ones in around 36% of the cases, while mutation testing achieves less than 4%.

Fault seeding is typically used in empirical studies to evaluate and compare test techniques. Central to these techniques lies the hypothesis that artificially seeded faults involve some form of realistic properties and thus provide realistic experimental results. In an attempt to strengthen realism, a recent line of research uses machine learning techniques, such as deep learning and Natural Language Processing, to seed faults that look like (syntactically) real ones, implying that fault realism is related to syntactic similarity. This raises the question of whether seeding syntactically similar faults indeed results in semantically similar faults and, more generally whether syntactically dissimilar faults are far away (semantically) from the real ones. We answer this question by employing 4 state-of-the-art fault-seeding techniques (PiTest - a popular mutation testing tool, IBIR - a tool with manually crafted fault patterns, DeepMutation - a learning-based fault seeded framework and μBERT - a mutation testing tool based on the pre-trained language model CodeBERT) that operate in a fundamentally different way, and demonstrate that syntactic similarity does not reflect semantic similarity. We also show that 65.11%, 76.44%, 61.39% and 9.76% of the real faults of Defects4J V2 are semantically resembled by PiTest, IBIR, μBERT and DeepMutation faults, respectively.

Much of recent software-engineering research has investigated the naturalness of code, the fact that code, in small code snippets, is repetitive and can be predicted using statistical language models like n-gram. Although powerful, training such models on large code corpus can be tedious, time consuming and sensitive to code patterns (and practices) encountered during training. Consequently, these models are often trained on a small corpus and thus only estimate the language naturalness relative to a specific style of programming or type of project. To overcome these issues, we investigate the use of pre-trained generative language models to infer code naturalness. Pre-trained models are often built on big data, are easy to use in an out-of-the-box way and include powerful learning associations mechanisms. Our key idea is to quantify code naturalness through its predictability, by using state-of-the-art generative pre-trained language models. Thus, we suggest to infer naturalness by masking (omitting) code tokens, one at a time, of code-sequences, and checking the models’ ability to predict them. We explore three different predictability metrics; a) measuring the number of exact matches of the predictions, b) computing the embedding similarity between the original and predicted code, i.e., similarity at the vector space, and c) computing the confidence of the model when doing the token completion task regardless of the outcome. We implement this workflow, named CODEBERT-NT, and evaluate its capability to prioritize buggy lines over non-buggy ones when ranking code based on its naturalness. Our results, on 2,510 buggy versions of 40 projects from the SmartShark dataset, show that CODEBERTNT outperforms both, random-uniform and complexity-based ranking techniques, and yields comparable results to the n-gram models.

Studying and exposing software vulnerabilities is important to ensure software security, safety, and reliability. Software engineers often inject vulnerabilities into their programs to test the reliability of their test suites, vulnerability detectors, and security measures. However, state-of-the-art vulnerability injection methods only capture code syntax/patterns, they do not learn the intent of the vulnerability and are limited to the syntax of the original dataset. To address this challenge, we propose the first intent-based vulnerability injection method that learns both the program syntax and vulnerability intent. Our approach applies a combination of NLP methods and semantic-preserving program mutations (at the bytecode level) to inject code vulnerabilities. Given a dataset of known vulnerabilities (containing benign and vulnerable code pairs), our approach proceeds by employing semantic-preserving program mutations to transform the existing dataset to semantically similar code. Then, it learns the intent of the vulnerability via neural machine translation (Seq2Seq) models. The key insight is to employ Seq2Seq to learn the intent (context) of the vulnerable code in a manner that is agnostic of the specific program instance. We evaluate the performance of our approach using 1275 vulnerabilities belonging to five (5) CWEs from the Juliet test suite. We examine the effectiveness of our approach in producing compilable and vulnerable code. Our results show that IntJECT is effective, almost all (99%) of the code produced by our approach is vulnerable and compilable. We also demonstrate that the vulnerable programs generated by IntJECT are semantically similar to the withheld original vulnerable code. Finally, we show that our mutation-based data transformation approach outperforms its alternatives, namely data obfuscation and using the original data.

Previous research has shown that artificial faults can be useful in many software engineering tasks such as testing, fault-tolerance assessment, debugging, dependability evaluation, risk analysis, etc. However, such artificial-fault-based applications can be questioned or inaccurate when the considered faults misrepresent real bugs. Since typically, fault injection techniques (i.e. mutation testing) produce a large number of faults by altering ”blindly” the code in arbitrary locations, they are unlikely capable to produce few but relevant real-like faults. In our work, we tackle this challenge by guiding the injection towards resembling bugs that have been previously introduced by developers. For this purpose, we propose iBiR, the first fault injection approach that leverages information from bug reports to inject ”realistic” faults. iBiR injects faults on the locations that are more likely to be related to a given bug-report by applying appropriate inverted fix-patterns, which are manually or automatically crafted by automated-program-repair researchers. We assess our approach using bugs from the Defects4J dataset and show that iBiR outperforms significantly conventional mutation testing in terms of injecting faults that semantically resemble and couple with real ones, in the vast majority of the cases. Similarly, the faults produced by iBiR give significantly better fault-tolerance estimates than conventional mutation testing in around 80% of the cases.

Current Java Virtual Machine (JVM) fuzzers aim at generating syntactically valid Java programs, without targeting any particular use of the standard Java library. While effective, such fuzzers fail to discover specific kinds of bugs or vulnerabilities, such as type confusion, that are related to the standard API usage. To deal with this issue, we introduce a mutation-based feedback-guided black-box JVM fuzzer, called Confuzzion. Confuzzion, as the name suggests, targets security-relevant object-oriented flaws with a particular focus on type confusion vulnerabilities. We show that in less than 4 hours, on commodity hardware and without any predefined initialization seed, Confuzzion automatically generates Java programs that reveal JVM vulnerabilities, i.e., the Common Vulnerabilities and Exposures CVE-2017-3272. We also show that state-of-the-art fuzzers or even traditional automatic testing techniques are not capable of detecting such faults, even after 48 hours of execution in the same environment. To the best of our knowledge, Confuzzion is the first fuzzer able to detect JVM type confusion vulnerabilities.