Agentic Coding, powered by autonomous agents such as GitHub Copilot and Cursor, enables developers to generate code, tests, and pull requests from natural language instructions alone. While this accelerates implementation, it produces larger volumes of code per pull request, shifting the burden from implementers to reviewers. In practice, a notable portion of AI-generated code is eventually deleted during review, yet reviewers must still examine such code before deciding to remove it. No prior work has explored methods to help reviewers efficiently identify code that will be removed.

The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request description characteristics, and how human reviewers respond to them, remains underexplored. In this study, we conduct an empirical analysis of pull requests created by five AI coding agents using the AIDev dataset. We analyze agent differences in pull request description characteristics, including structural features, and examine human reviewer response in terms of review activity, response timing, sentiment, and merge outcomes. We find that AI coding agents exhibit distinct PR description styles, which are associated with differences in reviewer engagement, response time, and merge outcomes. We observe notable variation across agents in both reviewer interaction metrics and merge rates. These findings highlight the role of pull request presentation and reviewer interaction dynamics in human–AI collaborative software development.

As an inexpensive, scalable Internet-access wireless network, we have studied the architecture, protocols, and design optimizations of the wireless Internet-access mesh network (WIMNET) that uses multiple access-points (APs) connected by wireless links. WIMNET can improve the dependability to failures of links and/or APs by allocating APs redundantly in the network field. Because redundant APs can increase the operational cost and degrade the performance due to increasing radio interferences, only the necessary APs for connections between the hosts and the Internet gateway should be activated at communications. Previously, we have defined the active AP selection problem of selecting the minimum number of active APs, and proposed its heuristic algorithm, assuming that every link has the same constant speed for simplicity. However, our preliminary experiments found that the link speed is greatly affected by the distance in the high-speed IEEE802.11n protocol that has recently become popular. In this paper, we propose a modification of the active AP selection algorithm to consider the link speed change, where we introduce a new design parameter for WIMNET to select links that have sufficient throughputs. We verify the effectiveness of our modified algorithm through simulations in four instances using the WIMNET simulator.

With the advancement of communication technologies, multiple wireless local area networks (WLANs) have been often deployed in the same building for the Internet access services. Usually, their wireless access-points (APs) have been independently managed and operated by different groups such as departments or laboratories in a university or a company. Then, a WLAN user host can detect signals from multiple APs, which may increase the interference and the operation cost, and degrade the communication performance. As a result, the number of active APs should be minimized by aggregating the physical APs allocated in the field into limited ones by introducing virtual APs. In this paper, we define this AP aggregation problem for WLANs that aggregates the APs into the minimum number of active APs, and prove the NP-completeness of its decision problem. Then, we propose the heuristic algorithm that is composed of five phases. The effectiveness of our proposal has been verified through simulations in two instances.

While volunteer computing (VC) systems reach the most powerful computing platforms, they still have the problem of guaranteeing computational correctness, due to the inherent unreliability of volunteer participants. Spot-checking technique, which checks each participant by allocating spotter jobs, is a promising approach to the validation of computation results. The current spot-checking technique and associated sabotage-tolerance methods are based on the implicit assumption that participants never detect the allocation of spotter jobs, however generating such spotter jobs is still an open problem. Hence, in the real VC environment where the implicit assumption does not always hold, spot-checking-based sabotage-tolerance methods (such as well-known credibility-based voting) become almost impossible to guarantee the computational correctness. In this paper, we generalize the spot-checking technique by introducing the idea of imperfect checking. Using our new technique, it becomes possible to estimate the correct credibility for participant nodes even if they may detect spotter jobs. Moreover, by the idea of imperfect checking, we propose a new credibility-based voting which does not need to allocate spotter jobs. Simulation results show that the proposed method reduces the computation time compared to the original credibility-based voting, while guaranteeing the same level of computational correctness.

This paper proposes an optimization technique for spot- checking to minimize the computation time of volunteer computing (VC) systems with malicious participants who return erroneous results. Credibility based voting with spot-checking is one of the most promising approaches to guaranteeing the computational correctness ofVC systems. There is an optimal value for the spot-check rate; that is, the increase of the spot-check rate reduces the necessary redundancy for voting, which in turn increases the computation time due to the spot-checking itself. From this tradeoff, the estimation of the optimal spot-check rate is therefore the major concern to minimize the computation time required for the credibility based voting. The key idea is to represent the mathematical expectation of the computation time as a function of the spot-check rate to obtain an estimate of the optimal spot-check rate before the computation. It is shown by Monte Carlo simulations that the proposed technique can always obtain an approximate estimate of the optimal spot-check rate and minimize the computation time with an uncertainty of 1 %.