Fault Localization (FL) is one of the most essential and time-consuming steps during software debugging. Mutation-based fault localization (MBFL) is one FL technique that has demonstrated promising fault localization accuracy in recent years. Current MBFL techniques mainly use First-Order Mutant (FOM) to localize faults, and only perform well in simple fault localization. When facing complex fault localization, MBFL with FOMs can only achieve low FL accuracy. Moreover, previous Higher-Order Mutant (HOM) generation techniques only use simple combinations of FOMs but do not consider the correlation between simple faults in the composition of complex faults. In this study, we consider the relationships between single faults and propose SFClu, a novel HOM generation method. Specifically, SFClu aims to generate HOMs to simulate complex faults consisting of multiple unrelated simple faults on multiple lines. To evaluate the performance of our proposed methods, we conduct empirical studies on 237 complex-fault programs from two datasets. The experimental results show that SFClu significantly outperforms traditional HOM generation methods (i.e., Last2First, DifferentOperators, and RandomMix). Furthermore, the experimental results also demonstrate that Higher-Order MBFL(HMBFL) with SFClu can outperform the state-of-the-art SBFL and MBFL techniques in terms of EXAM, TOP-N, and MAP metrics.

MBFL (Mutation-Based Fault Localization) is one of the most commonly studied fault localization techniques due to its promising fault localization effectiveness. However, MBFL incurs a high execution cost as it needs to execute the test suite on a large number of mutants. While previous studies have proposed mutant reduction methods for FOMs (First-Order Mutants) to help alleviate the cost of MBFL, the reduction of HOMs (Higher-Order Mutants) has not been thoroughly investigated. In this study, we propose SGS (Statement Granularity Sampling), a method which conducts HOMs reduction for HMBFL (Higher-Order Mutation-Based Fault Localization). Considering the relationship between HOMs and statements, we sample HOMs at the statement level to ensure each statement has corresponding HOMs. We empirically evaluate the fault localization effectiveness of HMBFL using SGS on 237 multiple-fault programs taken from the SIR and Codeflaws benchmarks. The experimental results show that (1) The best sampling ratio for HMBFL with SGS is 20%, which preserves the performance and reduces execution costs by 80% ; (2) The fault localization accuracy of HMBFL with SGS outperforms the state-of-the-art SBFL (Spectrum-Based Fault Localization) and MBFL techniques by 20%.

Automated code comment generation technologies can help developers understand code intent, which can significantly reduce the cost of software maintenance and revision. The latest studies in this field mainly depend on deep neural networks, such as convolutional neural networks and recurrent neural network. However, these methods may not generate high-quality and readable code comments due to the long-term dependence problem, which means that the code blocks used to summarize information are far from each other. Owing to the long-term dependence problem, these methods forget the previous input data’s feature information during the training process. In this article, to solve the long-term dependence problem and extract both the text and structure information from the program code, we propose a novel improved-Transformer-based comment generation method, named SeTransformer. Specifically, the SeTransformer utilizes the code tokens and an abstract syntax tree (AST) of programs to extract information as the inputs, and then, it leverages the self-attention mechanism to analyze the text and structural features of code simultaneously. Experimental results based on public corpus gathered from large-scale open-source projects show that our method can significantly outperform five state-of-the-art baselines (such as Hybrid-DeepCom and AST-attendgru). Furthermore, we also conduct a questionnaire survey for developers, and the results show that the SeTransformer can generate higher quality comments than those of other baselines.

In the aftermath of COVID-19, remote working has become the norm, and graduates now need an even wider range of skills, which traditional classrooms and internships do not always provide. Working in multiple time zones, within global multi-cultural teams, and only ever meeting colleagues through online technology are just some of the challenges, which require a new type of global graduate. Transversal skills including leadership, collaboration, innovation, digital, green, organization and communication skills are critical. The disruption from COVID-19 also presents unprecedented opportunities to develop more inclusive approaches to internships and international experiences, to level the playing field for students with special needs, from underrepresented groups or with caring commitments. In this position paper, we present a new Global Innovation internship model that has the aim of allowing students to complete technology internships and projects by working together virtually on real world challenges, guided by experienced industry and academic mentors. The model is being developed as part of an Erasmus+ funded project, and the partnership includes seven Higher Education Institutions from six different countries around the world. This position paper describes the design and development of a pilot programme of the Global Innovations internship model.

To improve the efficiency of the fault localization process, different automatic fault localization approaches have been proposed. Among these approaches, the spectrum-based fault localization (SBFL) approach has been widely used and studied due to its lightweight and high effectiveness. However, while the existence of coincidental correct (CC) test cases can influence the usefulness of SBFL in single-fault programs, their influence on multiple fault programs has not been thoroughly investigated. Therefore, in this article, we conduct a theoretical analysis and an empirical study to investigate the effect of CC test cases on multiple fault localization. The theoretical analysis is based on a suspiciousness calculation formula of SBFL, which divides CC test cases into three categories (specific, irrelevant, and unspecific) according to their association with a specific faulty statement. Following this analysis, we conduct an empirical study on two well-known open-source repositories (SIR and Defects4J), and the experimental results verify the correctness of our theoretical analysis. Specifically, reducing the number of specific CC test cases for a faulty statement can improve or maintain fault localization accuracy, while eliminating irrelevant CC test cases can have a negative effect. Finally, we design a CC test case identification solution based on the isolation-based multiple fault localization approach and demonstrate its effectiveness via a simulation experiment.