Comments are widely used in source code. If a comment is consistent with the code snippet it intends to annotate, it would aid code comprehension. Otherwise, Code Comment Inconsistency (CCI) is not only detrimental to the understanding of code, but more importantly, it would negatively impact the development, testing, and maintenance of software. To tackle this issue, existing research has been primarily focused on detecting inconsistencies with varied performance. It is evident that detection alone does not solve the problem; it merely paves the way for solving it. A complete solution requires detecting inconsistencies and, more importantly, rectifying them by amending comments. However, this type of work is scarce. In this paper, we contribute C4RLLaMA, a fine-tuned large language model based on the open-source CodeLLaMA. It not only has the ability to rectify inconsistencies by correcting relevant comment content but also outperforms state-of-the-art approaches in detecting inconsistencies. Experiments with various datasets confirm that C4RLLaMA consistently surpasses both post hoc and just-in-time CCI detection approaches. More importantly, C4RLLaMA outperforms substantially the only known CCI rectification approach in terms of multiple performance metrics. To further examine C4RLLaMA's efficacy in rectifying inconsistencies, we conducted a manual evaluation, and the results showed that the percentage of correct comment updates by C4RLLaMA was 65.0% and 55.9% in just-in-time and post hoc, respectively, implying C4RLLaMA's real potential in practical use.

Log statements have become an integral part of modern software systems.Prior research efforts have focused on supporting the decisions of placing log statements, such as where/what to log.With the increasing adoption of Large Language Models (LLMs) for coderelated tasks such as code completion or generation, automated approaches for generating log statements have gained much momentum.However, the performance of these approaches still has a long way to go.This paper explores enhancing the performance of LLM-based solutions for automated log statement generation by post-training LLMs with a purpose-built dataset.Thus the primary contribution is a novel approach called AUCAD, which automatically constructs such a dataset with information extracting from log-related issues.Researchers have long noticed that a significant portion of the issues in the open-source community are related to log statements.However, distilling this portion of data requires manual efforts, which is labor-intensive and costly, rendering it impractical.Utilizing our approach, we automatically extract logrelated issues from 1,537 entries of log data across 88 projects and identify 808 code snippets (i.e., methods) with retrievable source code both before and after modification of each issue (including log statements) to construct a dataset.Each entry in the dataset consists of a data pair representing high-quality and problematic log statements, respectively.With this dataset, we proceed to post-train multiple LLMs (primarily from the Llama series) for automated * Corresponding author.

As code review is a tedious and costly software quality practice, researchers have proposed several machine learning-based methods to automate the process. The primary focus has been on accuracy, that is, how accurately the algorithms are able to detect issues in the code under review. However, human intervention still remains inevitable since results produced by automated code review are not 100% correct. To assist human reviewers in making their final decisions on automatically generated review comments, the comprehensibility of the comments underpinned by accurate localization and relevant explanations for the detected issues with repair suggestions is paramount. However, this has largely been neglected in the existing research. Large language models (LLMs) have the potential to generate code review comments that are more readable and comprehensible by humans, thanks to their remarkable processing and reasoning capabilities. However, even mainstream LLMs perform poorly in detecting the presence of code issues because they have not been specifically trained for this binary classification task required in code review. In this article, we contribute Comprehensibility of Automated Code Review using Large Language Models ( Carllm ), a novel fine-tuned LLM that has the ability to improve not only the accuracy but, more importantly, the comprehensibility of automated code review, as compared to state-of-the-art pre-trained models and general LLMs.

Code review is an important practice in software development. One of its main objectives is for the assurance of code quality. For this purpose, the efficacy of code review is subject to the credibility of reviewers, i.e., reviewers who have demonstrated strong evidence of previously making quality-enhancing comments are more credible than those who have not. Code reviewer recommendation (CRR) is designed to assist in recommending suitable reviewers for a specific objective and, in this context, assurance of code quality. Its performance is susceptible to the relevance of its training dataset to this objective, composed of all reviewers’ historical review comments, which, however, often contains a plethora of comments that are irrelevant to the enhancement of code quality. Furthermore, recommendation accuracy has been adopted as the sole metric to evaluate a recommender's performance, which is inadequate as it does not take reviewers’ relevant credibility into consideration. These two issues form the ground truth problem in CRR as they both originate from the relevance of dataset used to train and evaluate CRR algorithms. To tackle this problem, we first propose the concept of Quality-Enhancing Review Comments (QERC), which includes three types of comments - change-triggering inline comments, informative general comments, and approve-to-merge comments. We then devise a set of algorithms and procedures to obtain a distilled dataset by applyingQERCto the original dataset. We finally introduce a new metric – reviewer's credibility for quality enhancement (RCQE) – as a complementary metric to recommendation accuracy for evaluating the performance of recommenders. To validate the proposed QERC-based approach to CRR, we conduct empirical studies using real data from seven projects containing over 82K pull requests and 346K review comments. Results show that: (a)QERCcan effectively address the ground truth problem by distilling quality-enhancing comments from the dataset containing original code reviews, (b)QERCcan assist recommenders in finding highly credible reviewers at a slight cost of recommendation accuracy, and (c) even “wrong” recommendations using the distilled dataset are likely to be more credible than those using the original dataset.

The microservice architecture has been commonly adopted by large scale software systems exemplified by a wide range of online services. Service monitoring through anomaly detection and root cause analysis (RCA) is crucial for these microservice systems to provide stable and continued services. However, compared with monolithic systems, software systems based on the layered microservice architecture are inherently complex and commonly involve entities at different levels of granularity. Therefore, for effective service monitoring, these systems have a special requirement of multi-granular RCA. Furthermore, as a large proportion of anomalies in microservice systems pertain to problematic code, to timely troubleshoot these anomalies, these systems have another special requirement of RCA at the finest code-level. Microservice systems rely on telemetry data to perform service monitoring and RCA of service anomalies. The majority of existing RCA approaches are only based on a single type of telemetry data and as a result can only support uni-granular RCA at either application-level or service-level. Although there are attempts to combine metric and tracing data in RCA, their objective is to improve RCA's efficiency or accuracy rather than to support multi-granular RCA. In this article, we propose a new RCA solutionTrinityRCLthat is able to localize the root causes of anomalies at multiple levels of granularity including application-level, service-level, host-level, and metric-level, with the unique capability of code-level localization by harnessing all three types of telemetry data to construct a causal graph representing the intricate, dynamic, and nondeterministic relationships among the various entities related to the anomalies. By implementing and deployingTrinityRCLin a real production environment, we evaluateTrinityRCLagainst two baseline methods and the results show thatTrinityRCLhas a significant performance advantage in terms of accuracy at the same level of granularity with comparable efficiency and is particularly effective to support large-scale systems with massive telemetry data.

This paper presents the design, implementation, and evaluation of Shrimp, an underwater visible light communication (VLC) system. To address the unique issues in underwater environment such as water flow and scattered sunlight interference, we exploit the circularly polarized light (CPL) and double links for underwater VLC transmission. A coding scheme tailored for underwater communication based on double CPL design is developed. We prototype Shrimp on commercial-off-the-shelf (COTS) LEDs with fabricated printed circuit boards (PCBs). Extensive experiments conducted in an indoor water pool, a lake, and the sea demonstrate that Shrimp can combat against environmental interference and achieve robust communication in underwater environments. The communication distance can be up to 3 m in sea/lake water using a 3 W commodity LED, outperforming the VLC schemes designed for in-air communication.