A. Ananda Rao

Anantha S. Rao, Barnaby van Straaten, Valentin John et al.

The performance and scalability of semiconductor quantum-dot (QD) qubits are limited by electrostatic drift and charge noise that shift operating points and destabilize qubit parameters. As systems expand to large one- and two-dimensional arrays, manual recalibration becomes impractical, creating a need for autonomous stabilization frameworks. Here, we introduce a method that uses the full network of charge-transition lines in repeatedly acquired double-quantum-dot charge stability diagrams (CSDs) as a multidimensional probe of the local electrostatic environment. By accurately tracking the motion of selected transitions in time, we detect voltage drifts, identify abrupt charge reconfigurations, and apply compensating updates to maintain stable operating conditions. We demonstrate our approach on a 10-QD device, showing robust stabilization and real-time diagnostic access to dot-specific noise processes. The high acquisition rate of radio-frequency reflectometry CSD measurements also enables time-domain noise spectroscopy, allowing the extraction of noise power spectral densities, the identification of two-level fluctuators, and the analysis of spatial noise correlations across the array. From our analysis, we find that the background noise at 100~$μ$\si{\hertz} is dominated by drift with a power law of $1/f^2$, accompanied by a few dominant two-level fluctuators and an average linear correlation length of $(188 \pm 38)$~\si{\nano\meter} in the device. These capabilities form the basis of a scalable, autonomous calibration and characterization module for QD-based quantum processors, providing essential feedback for long-duration, high-fidelity qubit operations.

Rudra Kumar M, A Ananda Rao

Exploring the impact of change requests applied to a software maintenance project helps to assess the fault-proneness of the change request to be handled further, which is perhaps a bug fix or even a new feature demand. In practice, the major development community stores change requests and related data using bug tracking systems such as Bugzilla. These data, together with the data stored in a versioning system, such as Concurrent Versioning Systems, are a valuable source of information to create descriptions and also can perform useful analyzes. In our earlier work, we proposed a novel statistical bipartite weighted graph-based approach to assessing the degree of fault-proneness of the change request and Change Request artifacts. With the motivation gained from this model, here we propose a novel strategy that estimates the degree of fault-proneness of a change request by assessing the impact of a change request artifact towards fault-proneness that considers the correlation between change requests artifact as another factor, which is in addition to our earlier strategy. The proposed model can be titled as Assessing the Fault Proneness Degree of Change Request Artifacts by estimating the impact of change requests correlation (DFP-CRC). As stated in our earlier model, the method DFP-CRC also makes use of information retrieval methods to identify the change request artifacts of the devised change request. And further evaluates the degree of fault-proneness of the Change Requests by estimating the correlation between change requests. The proposed method is evaluated by applying on concurrent versioning and Change request logs of the production level maintenance project.

Merugu. Gopichand, A. Ananda Rao

Identification of non-functional requirements is important for successful development and deployment of the software product. The acceptance of the software product by the customer depends on the non-functional requirements which are incorporated in the software. For this, we need to identify all the non-functional requirements required by all stakeholders. In the literature not many approaches are available for this purpose. Hence, we have proposed a four layered analysis approach for identification of non-functional requirements. The proposed layered approach has many advantages over non-layered approach. As part of this approach some rules are also proposed to be used in each layer. The approach is applied successfully on two case studies. The identified non-functional requirements are validated using a check list and in addition the completeness of the identified non-requirements is computed using a metric.

A. Ananda Rao, K. Narendar Reddy

Object oriented software with low cohesive classes can increase maintenance cost. Low cohesive classes are likely to be introduced into the software during initial design due to deviation from design principles and during evolution due to software deterioration. Low cohesive class performs operations that should be done by two or more classes. The low cohesive classes need to be identified and refactored using extract class refactoring to improve the cohesion. In this regard, two aspects are involved; the first one is to identify the low cohesive classes and the second one is to identify the clusters of concepts in the low cohesive classes for extract class refactoring. In this paper, we propose metrics supplemented agglomerative clustering technique for covering the above two aspects. The proposed metrics are validated using Weyuker's properties. The approach is applied successfully on two examples and on a case study.