As a critical and challenging research area within multi-agent systems (MAS), distributed multi-agent task allocation (D-MATA) has motivated extensive study and application across diverse domains. Although several systematic reviews exist on multi-agent task allocation (MATA), none provide an in-depth, systematic analysis of the core dimensions of D-MATA, namely applications, problems, methods, and metrics, nor explore their interrelationships. Moreover, no comprehensive repository capturing such foundational knowledge is currently available. These gaps collectively hinder the effective learning, adoption, and further development of D-MATA knowledge. To fill this gap, we conduct a systematic literature review (SLR) of 107 D-MATA studies, examining them from the perspectives of these core dimensions and discussing the potential interrelationships among these dimensions. To better support comprehensive evaluation and cross-study comparison of D-MATA methods, we propose a multi-dimensional evaluation framework based on the metrics used in the selected literature. Additionally, we provide an open knowledge repository comprising 107 problem-method-evaluation entries derived from the selected literature, supporting reproducible in-depth research. This work delivers clear guidance for both researchers and practitioners while building a systematic knowledge foundation for future investigations in the field.

Software architectures are usually meticulously designed to address multiple quality concerns and support long-term maintenance. However, there may be a lack of motivation for developers to document design rationales (i.e., the design alternatives and the underlying arguments for making or rejecting decisions) when they will not gain immediate benefit, resulting in a lack of standard capture of these rationales. With the turnover of developers, the architecture inevitably becomes eroded. This issue has motivated a number of studies to extract design knowledge from open-source communities in recent years. Unfortunately, none of the existing research has successfully extracted solutions alone with their corresponding arguments due to challenges such as the intricate semantics of online discussions and the lack of benchmarks for design rationale extraction.

Automatic Program Repair (APR) endeavors to autonomously rectify issues within specific projects, which generally encompasses three categories of tasks: bug resolution, new feature development, and feature enhancement. Despite extensive research proposing various methodologies, their efficacy in addressing real issues remains unsatisfactory. It's worth noting that, typically, engineers have design rationales (DR) on solution--- planed solutions and a set of underlying reasons---before they start patching code. In open-source projects, these DRs are frequently captured in issue logs through project management tools like Jira. This raises a compelling question: How can we leverage DR scattered across the issue logs to efficiently enhance APR?

CRISPR/Cas9 is a promising RNA-guided genome editing technology, which consists of a Cas9 nuclease and a single-guide RNA (sgRNA). So far, a number of sgRNA prediction softwares have been developed. However, they were usually designed for protein-coding genes without considering that long non-coding RNA (lncRNA) genes may have different characteristics. In this study, we first evaluated the performances of a series of known sgRNA-designing tools in the context of both coding and non-coding datasets. Meanwhile, we analyzed the underpinnings of their varied performances on the sgRNA's specificity for lncRNA including nucleic acid sequence, genome location and editing mechanism preference. Furthermore, we introduce a support vector machine-based machine learning algorithm named CRISPRlnc, which aims to model both CRISPR knock-out (CRISPRko) and CRISPR inhibition (CRISPRi) mechanisms to predict the on-target activity of targets. CRISPRlnc combined the paired-sgRNA design and off-target analysis to achieve one-stop design of CRISPR/Cas9 sgRNAs for non-coding genes. Performance comparison on multiple datasets showed that CRISPRlnc was far superior to existing methods for both CRISPRko and CRISPRi mechanisms during the lncRNA-specific sgRNA design. To maximize the availability of CRISPRlnc, we developed a web server (http://predict.crisprlnc.cc) and made it available for download on GitHub.