Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N.

Creative Approaches for Thermal Hot Spot Identification on Analog IC

With the increase in the complexity of semiconductor wafer fabrication processes, the timing in responding and discovering the failure mechanism to a product failure at the initial product development stage or at the end of production line becomes a crucial factor. Effectively utilization the fault localization technique such as Photon Emission Microscopy (PEM), Laser Signal Injection Microscopy (LSIM) and Thermal Hotspot Localization (THS) may be significantly shortened the cycle time in the fault localization process. This paper will illustrate the creative approaches for thermal hot spot identification using modulated THS technique coupled with modified external electrical connection.