@tComment: testing Javadoc comments to detect comment-code inconsistencies

Code comments are important artifacts in software. Javadoc comments are widely used in Java for API specifications. API developers write Javadoc comments, and API users often read these comments to understand the API they use, e.g., an API user can read a Javadoc comment for a method instead of reading the method body of the method. An inconsistency between the Javadoc comment and body for a method indicates either a fault in the body or, effectively, a fault in the comment that can mislead the method callers of the method to introduce faults in their code. This thesis presents a novel approach, called @tComment, for testing Javadoc comments, specifically for testing method properties about null values and related exceptions. Our approach consists of two components. The first component takes as input source files for a Java project and automatically analyzes the English text in Javadoc comments to infer a set of likely properties for a method in the files. The second component generates random tests for these methods, checks the inferred properties, and reports inconsistencies. We evaluated @tComment on seven open-source projects and found 28 inconsistencies between Javadoc comments and method bodies. We reported all inconsistencies, and 12 have already been confirmed and fixed by the developers

EFFL: Egalitarian Fairness in Federated Learning for Mitigating Matthew Effect

Recent advances in federated learning (FL) enable collaborative training of machine learning (ML) models from large-scale and widely dispersed clients while protecting their privacy. However, when different clients' datasets are heterogeneous, traditional FL mechanisms produce a global model that does not adequately represent the poorer clients with limited data resources, resulting in lower accuracy and higher bias on their local data. According to the Matthew effect, which describes how the advantaged gain more advantage and the disadvantaged lose more over time, deploying such a global model in client applications may worsen the resource disparity among the clients and harm the principles of social welfare and fairness. To mitigate the Matthew effect, we propose Egalitarian Fairness Federated Learning (EFFL), where egalitarian fairness refers to the global model learned from FL has: (1) equal accuracy among clients; (2) equal decision bias among clients. Besides achieving egalitarian fairness among the clients, EFFL also aims for performance optimality, minimizing the empirical risk loss and the bias for each client; both are essential for any ML model training, whether centralized or decentralized. We formulate EFFL as a constrained multi-constrained multi-objectives optimization (MCMOO) problem, with the decision bias and egalitarian fairness as constraints and the minimization of the empirical risk losses on all clients as multiple objectives to be optimized. We propose a gradient-based three-stage algorithm to obtain the Pareto optimal solutions within the constraint space. Extensive experiments demonstrate that EFFL outperforms other state-of-the-art FL algorithms in achieving a high-performance global model with enhanced egalitarian fairness among all clients

Antiviral properties of Berembang Bukit and Kandis Hutan against psedorabies virus in animal cell culture

Tropical rainforest in Malaysia represents and untapped potential source of antiviral compounds. Bioactive compounds in plant species from the same genus as Kandis Hutan such as xanthones, benzophenones, biflavonoids and lupeol had been studied. Eugeniin is an anti-herpesvirus compound which had also been found n Berembang Bukit. This preliminary study was carried out to discover the presence of antiviral properties in Berembang Bukit and Kandis Hutan using different antiviral assays. In this study, MTT cell viability assay was used in addition to microscopic evaluation of pseudorabies virus (PrV)- induced cytopathic effects (CPE) on Vero cells. The cellular toxicity of DMSO was also evaluated. DMSO was less than 10% cytotoxic at concentration of 0.1% to Vero cells and its effect can be negligible. Both plants had demonstrated antiviral properties in thyl acetate and ethanol extracts. From our findings from all three antiviral assays, the ethanol-extracted Kandis Hutan possessed the most promising antiviral properties. Nevertheless, antiviral potential of ethyl acetate and ethanol-extracted Berembang Bukit and ethyl acetate-extracted Kandis Hutan also merit further investigatio

Diversifying focused testing for unit testing

Software changes constantly because developers add new features or modifications. This directly affects the effectiveness of the testsuite associated with that software, especially when these new modifications are in a specific area that no test case covers. This paper tackles the problem of generating a high quality test suite to cover repeatedly a given point in a program, with the ultimate goal of exposing faults possibly affecting the given program point. Both search based software testing and constraint solving offer ready, but low quality, solutions to this: ideally a maximally diverse covering test set is required whereas search and constraint solving tend to generate test sets with biased distributions. Our approach, Diversified Focused Testing (DFT), uses a search strategy inspired by GödelTest. We artificially inject parameters into the code branching conditions and use a bi-objective search algorithm to find diverse inputs by perturbing the injected parameters, while keeping the path conditions still satisfiable. Our results demonstrate that our technique, DFT, is able to cover a desired point in the code at least 90% of the time. Moreover, adding diversity improves the bug detection and the mutation killing abilities of the test suites. We show that DFT achieves better results than focused testing, symbolic execution and random testing by achieving from 3% to 70% improvement in mutation score and up to 100% improvement in fault detection across 105 software subjects

DESIGN OF REPAIR OPERATORS FOR AUTOMATED PROGRAM REPAIR

Ph.DDOCTOR OF PHILOSOPHY (SOC

F1X at APR-COMP 2024

This is a snapshot of F1X (https://github.com/mechtaev/f1x) used at APR-COMP 2024