Large Language Models (LLMs) have gained considerable traction within the Software Engineering (SE) community, impacting various SE tasks from code completion to test generation, from program repair to code summarization. Despite their promise, researchers must still be careful as numerous intricate factors can influence the outcomes of experiments involving LLMs. This paper initiates an open discussion on potential threats to the validity of LLM-based research including issues such as closed-source models, possible data leakage between LLM training data and research evaluation, and the reproducibility of LLM-based findings. In response, this paper proposes a set of guidelines tailored for SE researchers and Language Model (LM) providers to mitigate these concerns. The implications of the guidelines are illustrated using existing good practices followed by LLM providers and a practical example for SE researchers in the context of test case generation.@en

Over the last decades, various tools (e.g., AUSTIN and EvoSuite) have been developed to automate the process of unit-level test case generation. Most of these tools are designed for statically-typed languages, such as C and Java. However, as is shown in recent Stack Overflow developer surveys, the popularity of dynamicallytyped languages, such as JavaScript and Python, has been increasing and is dominating the charts. Only recently, tools for automated test case generation of dynamically-typed languages have started to emerge (e.g., Pynguin for Python). However, to the best of our knowledge, there is no tool that focuses on automated test case generation for server-side JavaScript. To this aim, we introduce SynTest-JavaScript, a user-friendly tool for automated unit-level test case generation for (server-side) JavaScript. To showcase the effectiveness of SynTest-JavaScript, we empirically evaluate it on five large open-source JavaScript projects@en

Many-objective evolutionary algorithms (MOEAs) have been applied in the software testing literature to automate the generation of test cases. While previous studies confirmed the superiority of MOEAs over other algorithms, one of the open challenges is maintaining a strong selective pressure considering the large number of objectives to optimize (coverage targets). This paper investigates four density estimators as a substitute for the traditional crowding distance. In particular, we consider two estimators previously proposed in the evolutionary computation community, namely the subvector-dominance assignment (SD) and the epsilon-dominance assignment (ED). We further propose two novel density estimators specific to test case generation, namely the token-based density estimator (TDE) and the path-based density estimator (PDE). Based on the CodeBERT model tokenizer, TDE uses natural language processing to measure the semantic distance between test cases. PDE, on the other hand, considers the distance between the source-code paths executed by the test cases. We evaluate these density estimators within EvoSuite on 100 non-trivial Java classes from the SF110 benchmark. Our results show that the proposed path-based density estimator (PDE) outperforms all other density estimators in enhancing mutation scores. It increases mutation scores by 4.26 % on average (with a max of over 60%) to the traditional crowding distance.@en

The microservice architecture allows developers to divide the core functionality of their software system into multiple smaller services. However, this architectural style also makes it harder for them to debug and assess whether the system's deployment conforms to its implementation. We present CATMA, an automated tool that detects non-conformances between the system's deployment and implementation. It automatically visualizes and generates potential interpretations for the detected discrepancies. Our evaluation of CATMA shows promising results in terms of performance and providing useful insights. CATMA is available at https://cyber-analytics.nl/catma.github.io/, and a demonstration video is available at https://youtu.be/WKP1hG-TDKc.@en

Compiler correctness is a cornerstone of reliable software development. However, systematic testing of compilers is infeasible, given the vast space of possible programs and the complexity of modern programming languages. In this context, differential testing offers a practical methodology as it addresses the oracle problem by comparing the output of alternative compilers given the same set of programs as input. In this paper, we investigate the effectiveness of differential testing in finding bugs within the Kotlin compilers developed at JetBrains. We propose a black-box generative approach that creates input programs for the K1 and K2 compilers. First, we build workable models of Kotlin semantic (semantic interface) and syntactic (enriched context-free grammar) language features, which are subsequently exploited to generate random code snippets. Second, we extend random sampling by introducing two genetic algorithms (GAs) that aim to generate more diverse input programs. Our case study shows that the proposed approach effectively detects bugs in K1 and K2; these bugs have been confirmed and (some) fixed by JetBrains developers. While we do not observe a significant difference w.r.t. the number of defects uncovered by the different search algorithms, random search and GAs are complementary as they find different categories of bugs. Finally, we provide insights into the relationships between the size, complexity, and fault detection capability of the generated input programs.@en

Writing software tests is laborious and time-consuming. To address this, prior studies introduced various automated test-generation techniques. A well-explored research direction in this field is unit test generation, wherein artificial intelligence (AI) techniques create tests for a method/class under test. While many of these techniques have primarily found applications in a research context, existing tools (e.g., EvoSuite, Randoop, and AthenaTest) are not user-friendly and are tailored to a single technique. This paper introduces TestSpark, a plugin for IntelliJ IDEA that enables users to generate unit tests with only a few clicks directly within their Integrated Development Environment (IDE). Furthermore, TestSpark also allows users to easily modify and run each generated test and integrate them into the project workflow. TestSpark leverages the advances of search-based test generation tools, and it introduces a technique to generate unit tests using Large Language Models (LLMs) by creating a feedback cycle between the IDE and the LLM. Since TestSpark is an open-source (https://github.com/JetBrains-Research/TestSpark), extendable, and well-documented tool, it is possible to add new test generation methods into the plugin with the minimum effort. This paper also explains our future studies related to TestSpark and our preliminary results. Demo video: https://youtu.be/0F4PrxWfiXo@en

Large Language Models (LLMs) have gained considerable traction within the Software Engineering (SE) community, impacting various SE tasks from code completion to test generation, from program repair to code summarization. Despite their promise, researchers must still be careful as numerous intricate factors can influence the outcomes of experiments involving LLMs.
This paper initiates an open discussion on potential threats to the validity of LLM-based research including issues such as closed-source models, possible data leakage between LLM training data and research evaluation, and the reproducibility of LLM-based findings.
In response, this paper proposes a set of guidelines tailored for SE researchers and Language Model (LM) providers to mitigate these concerns.
The implications of the guidelines are illustrated using existing good practices followed by LLM providers and a practical example for SE researchers in the context of test case generation.@en

Adversarial examples remain a critical concern for the robustness of deep learning models, showcasing vulnerabilities to subtle input manipulations. While earlier research focused on generating such examples using white-box strategies, later research focused on gradient-based black-box strategies, as models' internals often are not accessible to external attackers. This paper extends our prior work by exploring a gradient-free search-based algorithm for adversarial example generation, with particular emphasis on differential evolution (DE). Building on top of the classic DE operators, we propose five variants of gradient-free algorithms: a single-objective approach (GADE), two multi-objective variations (NSGA-IIDE and MOEA/DDE), and two many-objective strategies (NSGA-IIIDE and AGE-MOEADE). Our study on five canonical image classification models shows that whilst GADE variant remains the fastest approach, NSGA-IIDE consistently produces more minimal adversarial attacks (i.e., with fewer image perturbations). Moreover, we found that applying a post-process minimization to our adversarial images, would further reduce the number of changes and overall delta variation (image noise).@en

Cyber-Physical Systems (CPSs) have gained traction in recent years. A major non-functional quality of CPS is performance since it affects both usability and security. This critical quality attribute depends on the specialized hardware, simulation engines, and environmental factors that characterize the system under analysis. While a large body of research exists on performance issues in general, studies focusing on performance-related issues for CPSs are scarce. The goal of this paper is to build a taxonomy of performance issues in CPSs. To this aim, we present two empirical studies aimed at categorizing common performance issues (Study I) and helping developers detect them (Study II). In the first study, we examined commit messages and code changes in the history of 14 GitHub-hosted open-source CPS projects to identify commits that report and fix self-admitted performance issues. We manually analyzed 2699 commits, labeled them, and grouped the reported performance issues into antipatterns. We detected instances of three previously reported Software Performance Antipatterns (SPAs) for CPSs. Importantly, we also identified new SPAs for CPSs not described earlier in the literature. Furthermore, most performance issues identified in this study fall into two new antipattern categories: Hard Coded Fine Tuning (399 of 646) and Magical Waiting Number (150 of 646). In the second study, we introduce static analysis techniques for automatically detecting these two new antipatterns; we implemented them in a tool called AP-Spotter. We analyzed 9 open-source CPS projects not utilized to build the SPAs taxonomy to benchmark AP-Spotter. Our results show that AP-Spotter achieves 62.04% precision in detecting the antipatterns@en

Web Application Programming Interfaces (APIs) allow systems to be addressed programmatically and form the backbone of the internet. RESTful and RPC APIs are among the most common API architectures used. In the last decades, researchers have proposed various techniques for automated testing of RESTful APIs, however, to the best of the authors' knowledge there exists no work on testing JSON-RPC (one of the two data formats supported by RPC) APIs. To address this limitation, we propose a grammar-based evolutionary fuzzing approach for testing JSON-RPC APIs that uses a novel black-box heuristic. Specifically, we use a diversity-based fitness function based on hierarchical clustering to quantity the differences in API method responses. Our hypothesis is that responses that are unlike previously seen ones are an indication that new uncovered code paths are reached. We evaluate our approach on the XRP ledger, a large-scale industrial blockchain system that uses JSON-RPC APIs. Our results show that the proposed approach performs significantly better than the baseline (grammar-based fuzzer) and covers an additional 240 branches.

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Test case prioritization techniques have emerged as effective strategies to optimize this process and mitigate the regression testing costs. Commonly, black-box heuristics guide optimal test ordering, leveraging information retrieval (e.g., cosine distance) to measure the test case distance and sort them accordingly. However, a challenge arises when dealing with tests of varying granularity levels, as they may employ distinct vocabularies (e.g., name identifiers). In this paper, we propose to measure the distance between test cases based on the shortest path between their identifiers within the WordNet lexical database. This additional heuristic is combined with the traditional cosine distance to prioritize test cases in a multi-objective fashion. Our preliminary study conducted with two different Java projects shows that test cases prioritized with WordNet achieve larger fault detection capability (APFD _C ) compared to the traditional cosine distance used in the literature.

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Deep learning (DL) models are known to be highly accurate, yet vulnerable to adversarial examples. While earlier research focused on generating adversarial examples using whitebox strategies, later research focused on black-box strategies, as models often are not accessible to external attackers. Prior studies showed that black-box approaches based on approximate gradient descent algorithms combined with meta-heuristic search (i.e., the BMI-FGSM algorithm) outperform previously proposed white- and black-box strategies. In this paper, we propose a novel black-box approach purely based on differential evolution (DE), i.e., without using any gradient approximation method. In particular, we propose two variants of a customized DE with customized variation operators: (1) a single-objective (Pixel-SOO) variant generating attacks that fool DL models, and (2) a multi-objective variant (Pixel-MOO) that also minimizes the number of changes in generated attacks. Our preliminary study on five canonical image classification models shows that Pixel-SOO and Pixel-MOO are more effective than the state-of-the-art BMI-FGSM in generating adversarial attacks. Furthermore, Pixel-SOO is faster than Pixel-MOO, while the latter produces subtler attacks than its single-objective variant.@en

More machine learning (ML) models are introduced to the field of Software Engineering (SE) and reached a stage of maturity to be considered for real-world use; But the real world is complex, and testing these models lacks often in explainability, feasibility and computational capacities. Existing research introduced meta-morphic testing to gain additional insights and certainty about the model, by applying semantic-preserving changes to input-data while observing model-output. As this is currently done at random places, it can lead to potentially unrealistic datapoints and high computational costs. With this work, we introduce genetic search as an aid for metamorphic testing in SE ML. Exploiting the delta in output as a fitness function, the evolutionary intelligence optimizes the transformations to produce higher deltas with less changes. We perform a case study minimizing F1 and MRR for Code2Vec on a representative sample from java-small with both genetic and random search. Our results show that within the same amount of time, genetic search was able to achieve a decrease of 10% in F1 while random search produced 3% drop.

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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.In this article, we investigate test case prioritization techniques to increase the ability to detect SDC regression faults with virtual tests earlier. Our approach, called SDC-Prioritizer, prioritizes virtual tests for SDCs according to static features of the roads we designed to be used within the driving scenarios. These features can be collected without running the tests, which means that they do not require past execution results. We introduce two evolutionary approaches to prioritize the test cases using diversity metrics (black-box heuristics) computed on these static features. These two approaches, called SO-SDC-Prioritizer and MO-SDC-Prioritizer, use single-objective and multi-objective genetic algorithms (GA), respectively, to find trade-offs between executing the less expensive tests and the most diverse test cases earlier.Our empirical study conducted in the SDC domain shows that MO-SDC-Prioritizer significantly (P- value <=0.1e-10) improves the ability to detect safety-critical failures at the same level of execution time compared to baselines: random and greedy-based test case orderings. Besides, our study indicates that multi-objective meta-heuristics outperform single-objective approaches when prioritizing simulation-based tests for SDCs.MO-SDC-Prioritizer prioritizes test cases with a large improvement in fault detection while its overhead (up to 0.45% of the test execution cost) is negligible.

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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 generation approach that exploits how a pair of classes, the caller and the callee, interact with each other through method calls. In particular, Cling generates integration-level test cases that maximize the Coupled Branches Criterion (CBC). Coupled branches are pairs of branches containing a branch of the caller and a branch of the callee such that an integration test that exercises the former also exercises the latter. CBC is a novel integration-level coverage criterion, measuring the degree to which a test suite exercises the interactions between a caller and its callee classes. We implemented Cling and evaluated the approach on 140 pairs of classes from five different open-source Java projects. Our results show that (1) Cling generates test suites with high CBC coverage, thanks to the definition of the test suite generation as a many-objectives problem where each couple of branches is an independent objective; (2) such generated suites trigger different class interactions and can kill on average 7.7% (with a maximum of 50%) of mutants that are not detected by tests generated randomly or at the unit level; (3) Cling can detect integration faults coming from wrong assumptions about the usage of the callee class (25 for our subject systems) that remain undetected when using automatically generated random and unit-level test suites.

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We present HasBugs, an extensible and manually-curated dataset of real-world 25 Haskell Bugs from 6 open source repositories. We provide a faulty, tested, and fixed version of each bug in our dataset with reproduction packages, description, and bug context. For technical users, the dataset is meant to either help researchers adapt techniques from other programming languages to Haskell or to provide a human-verified gold standard for tools evaluation and enable future reproducibility. We also see applicability for qualitative research, e.g., by analysis of bug lifecycles and comparison to other languages. We provide a companion website for easy access and overview under https://ciselab.github.io/HasBugs/.@en

Spatial mode division de-multiplexing of optical signals has many real-world applications, such as quantum computing and both classical and quantum optical communication. In this context, it is crucial to develop devices able to efficiently sort optical signals according to the optical mode they belong to and route them on different paths. Depending on the mode selected, this problem can be very hard to tackle. Recently, researchers have proposed using multi-objective evolutionary algorithms (MOEAs) ---and NSGA-II in particular--- combined with Linkage Learning (LL) to automate the process of design mode sorter. However, given the very large-search scale of the problem, the existing evolutionary-based solutions have a very slow convergence rate. In this paper, we proposed a novel approach for mode sorter design that combines (1) stochastic linkage learning, (2) the adaptive geometry estimation-based MOEA (AGE-MOEA-II), and (3) an adaptive mutation operator. Our experiments with two- and three-objectives (beams) show that our approach is faster (better convergence rate) and produces better mode sorters (closer to the ideal solutions) than the state-of-the-art approach. A direct comparison with the vanilla NSGA-II and AGE-MOEA-II also further confirms the importance of adopting LL in this domain.@en

Blockchain systems are prone to concurrency bugs due to the nondeterminism in the delivery order of messages between the distributed nodes. These bugs are hard to detect since they can only be triggered by a specific order or timing of concurrent events in the execution.
Systematic concurrency testing techniques, which explore all possible delivery orderings of messages to uncover concurrency bugs, are not scalable to large distributed systems such as blockchains.
Random concurrency testing methods search for bugs in a randomly generated set of executions and offer a practical testing method.
In this paper, we investigate the effectiveness of random concurrency testing on blockchain systems using a case study on the XRP Ledger of the Ripple blockchain, which maintains one of the most popular cryptocurrencies in the market today. We test the Ripple consensus algorithm of the XRP Ledger by exploring different delivery orderings of consensus protocol messages.
Moreover, we design an evolutionary algorithm to guide the random test case generation toward certain system behaviors to discover concurrency bugs more efficiently.
Our case study shows that random concurrency testing is effective at detecting concurrency bugs in blockchains, and the evolutionary approach for test generation improves test efficiency.
Our experiments could successfully detect the bugs we seeded in the Ripple source code. Moreover, we discovered a previously unknown concurrency bug in the production implementation of Ripple.@en

Continuous Integration and Delivery (CI/CD) practices have shown several benefits for software development and operations, such as faster release cycles and early discovery of defects. For Cyber-Physical System (CPS) development, CI/CD can help achieving required goals, such as high dependability, yet it may be challenging to apply. This article empirically investigates challenges, barriers, and their mitigation occurring when applying CI/CD practices to develop CPSs in 10 organizations working in eight different domains. The study has been conducted through semi-structured interviews, by applying an open card sorting procedure together with a member-checking survey within the same organizations, and by validating the results through a further survey involving 55 professional developers. The study reveals several peculiarities in the application of CI/CD to CPSs. These include the need for (i) combining continuous and periodic builds while balancing the use of Hardware-in-the-Loop and simulators, (ii) coping with difficulties in software deployment (iii) accounting for simulators and Hardware-in-the-Loop differing in their behavior, and (vi) combining hardware/software expertise in the development team. Our findings open the road toward recommenders aimed at supporting the setting and evolution of CI/CD pipelines, as well as university curricula requiring interdisciplinarity, such as knowledge about hardware, software, and their interplay.

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Automatic program repair (APR) regularly faces the challenge of overfitting patches — patches that pass the test suite, but do not actually address the problems when evaluated manually. Currently, overfit detection requires manual inspection or an oracle making quality control of APR an expensive task. With this work, we want to introduce properties in addition to unit tests for APR to address the problem of overfitting. To that end, we design and im- plement PropR, a program repair tool for Haskell that leverages both property-based testing (via QuickCheck) and the rich type system and synthesis offered by the Haskell compiler. We compare the repair-ratio, time-to-first-patch and overfitting-ratio when using unit tests, property-based tests, and their combination. Our results show that properties lead to quicker results and have a lower overfit ratio than unit tests. The created overfit patches provide valuable insight into the underlying problems of the program to repair (e.g., in terms of fault localization or test quality). We consider this step towards fitter, or at least insightful, patches a critical contribution to bring APR into developer workflows.@en