Search time is an important metric for regional coverage searches conducted by unmanned clusters. This paper proposes an improved proximal policy optimization (IPPO) algorithm to decrease search time while ensuring the coverage rate for a heterogeneous cluster consisting of unmanned ground vehicles and unmanned aerial vehicles (UGV-UAV). The models of the UGV-UAV cluster, search scenario, and search constraints are first developed. Then, the IPPO algorithm is designed to simultaneously learn the cross-domain actions of UGVs and UAVs. The main advantage of the IPPO is that it can achieve the cross-domain cooperative learning (CDCL) mechanism, thus ensuring the collaboration consistency of the UGV-UAV cluster and enhancing search efficiency. To analyze the IPPO-based regional coverage search performance, three state-of-the-art (SOTA) methods are selected for comparison. The validation results demonstrate that the proposed method outperforms these SOTA methods in terms of both search time and coverage rate.

In many fields of software engineering, studies on human behavior have attracted a lot of attention; however, few such studies exist in automated debugging. Parnin and Orso conducted a pioneering study comparing the performance of programmers in debugging with and without a ranking-based fault localization technique, namely Spectrum-Based Fault Localization (SBFL). In this paper, we revisit the actual helpfulness of SBFL, by addressing some major problems that were not resolved in Parnin and Orso's study. Our investigation involved 207 participants and 17 debugging tasks. A user-friendly SBFL tool was adopted. It was found that SBFL tended not to be helpful in improving the efficiency of debugging. By tracking and analyzing programmers' focus of attention, we characterized their source code navigation patterns and provided in-depth explanations to the observations. Results indicated that (1) a short "first scan" on the source code tended to result in inefficient debugging; and (2) inspections on the pinpointed statements during the "follow-up browsing" were normally just quick skimming. Moreover, we found that the SBFL assistance may even slightly weaken programmers' abilities in fault detection. Our observations imply interference between the mechanism of automated fault localization and the actual assistance needed by programmers in debugging. To resolve this interference, we provide several insights and suggestions.

Customer care calls have served as a primary and direct channel for mobile users to communicate their needs with cellular service providers. These customer needs can be quite diverse and usually include provisioning/upgrade, account/billing, device and service performance related issues. The resolution process of these customer needs is largely manual and tedious. The resolution process not only affects the customer experience but could also entails cost on customer care and network operations of the mobile service providers. We present TREAT, which aims at automatically identifying opportunities for auto-resolving customer reported needs, and hence improving customer experiences and reducing cost for customer care and network operations.

Spectrum-Based Fault Localization (SBFL) techniques calculate risk values to predict buggy units in a program,but they may cause heavy manual work when the calculated risk values are not reasonable on some application scenarios. In this paper, presents a preliminary study to estimate the effectiveness of SBFL before manual code walk through, so that we can decide whether to adopt SBFL for a given application.

In this paper a hybrid one-bit feedback algorithm is proposed to achieve carrier phase alignment at the receiver for distributed transmit beamforming. The proposed iterative algorithm employs two schemes to speed up the convergence process, which exploit negative feedback information in a single time slot (Scheme 1) and in successive time slots (Scheme 2) respectively, whereas previously proposed algorithms in the literature discard this information. We show that the proposed algorithm yields a significant improvement in the convergence speed compared to the original algorithm. Furthermore, we modify the proposed algorithm to be capable of tracking time-varying channels which have variable rates of phase drift. The modified hybrid algorithm has the ability to adjust perturbation sizes adaptively without the knowledge of channel state information and is suited for practical implementations.

In this paper an improved iterative algorithm is proposed for distributed transmit beamforming to achieve carrier phase alignment at the receiver. The transmitters adjust their phase offsets based on one-bit feedback from the receiver in each time slot. The proposed algorithm has an improvement in the convergence speed of phase alignment compared to a previously proposed algorithm in the literature by exploiting one-bit feedback information more efficiently. Simulation results show that the improved algorithm on average has a 20% faster convergence speed. The minimum number of time slots required to achieve specified beamforming gains and the corresponding perturbation steps are obtained by Monte Carlo simulations.