Spectral‐based fault localization using hyperbolic function

Debugging is crucial for producing reliable software. One of the effective bug localization techniques is spectral‐based fault localization. It tries to locate a buggy statement by applying an evaluation metric to program spectra and ranking program components on the basis of the score it computes. Here, we propose a restricted class of “hyperbolic” metrics, with a small number of numeric parameters. This class of functions is based on past theoretical and empirical results. We show that optimization methods such as genetic programming and simulated annealing can reliably discover effective metrics over a wide range of data sets of program spectra. We evaluate the performance for both real programs and model programs with single bugs, multiple bugs, “deterministic” bugs, and nondeterministic bugs and find that the proposed class of metrics performs as well as or better than the previous best‐performing metrics over a broad range of data.     

Improving spectral‐based fault localization using static analysis

Debugging is crucial for producing reliable software. One of the effective bug localization techniques is spectral‐based fault localization (SBFL). It helps to locate a buggy statement by applying an evaluation metric to program spectra and ranking program components on the basis of the score it computes. SBFL is an example of a dynamic analysis – an analysis of computer program that is performed by executing it with sufficient number of test cases. Static analysis, on the other hand, is performed in a non‐runtime environment. We introduce a weighting technique by combining these two kinds of program analysis. Static analysis is performed to categorize program statements into different classes and giving them weights based on the likelihood of being buggy statement. Statements are finally ranked on the basis of the weights computed by statements' categorization (static analysis) and scores computed by SBFL metrics (dynamic analysis). We evaluate the performance of our technique on Siemens test suite and Flex (having seeded bugs seeded by expert developers), Sed (having mixture of real and seeded bugs), and Space (having real bugs). In our evaluation, proposed weighting technique improves the performance of a wide variety of fault localization metrics up to 20% on single bug datasets and up to 42% on multi‐bug datasets. Copyright © 2017 John Wiley & Sons, Ltd.     

Low-power analogue computational blocks based on high-performance floating-gate MOSFET resistor

This article proposes a new FGMOS-based programmable FGMOS resistor. A highly linear resistor is implemented by cancelling the non-term present in the drain current equation of MOSFET operating in the linear region. The inherited features of FGMOS resistor are simplicity, programmability, wider bandwidth and very low power dissipation without supply voltage. The power dissipation of the proposed FGMOS resistor is only 985 nW. Analogue computational blocks such as programmable reciprocal circuit, current to voltage converter and low-pass filter as applications of proposed programmable FGMOS resistor are also suggested. The power dissipation of reciprocal circuit and low-pass filter are 14.7 and 131 µW, respectively. To demonstrate the efficacy of the circuits, simulations are carried out using SPICE on 0.13 µm CMOS technology.