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?

A software architectural tactic is a design decision for realizing quality goals at the architectural level. With the evolution of code, the designed architectural tactics might be degraded over time. In practice, the existing systems provide limited support for checking the consistency between an architectural tactic and its implementation. Kim et al. specified the generic structure and interaction behavior for a subset of architectural tactics in Role-Based Meta-modeling Language (RBML) to facilitate the design of tactics. Based on Kim et al.'s work, this paper first presents a machine learning-based method to assist users in detecting the behavior methods of the tactic structure in code, then proposes a runtime verification (RV) method for checking the behavioral consistency between the tactic specification in RBML and its implementation. We conducted experiments for the behavioral methods detection approach by comparing five machine learning models on a dataset with seventy-four open-source projects containing ten types of tactics. For each tactic, we selected an open-source project to show the effectiveness of the RV approach. Finally, we design and implement a prototype tool named ArchTacRV to help developers efficiently maintain the architectural tactics.