Software Failure Prediction Based On Program State and First-Error Characteristics

Abstract

Evaluating program reliability against software faults involves the work of analyzing program failure against software faults as behavior on failures is fundamental for assessing reliability. Similar failure behavior reflects similar reliability assessment against software faults of same time–space distribution. Since program failure is determined by fault manifestations, working on similarities on fault manifestations helps figuring out failures in similarity as well. In this paper, we propose a novel method to characterize program behavior by defining fine-grained runtime states at assembly-level of code, aiming to capture the very first abnormal manifestation—first-error—after software fault being activated during program run. Failure prediction model and measurement is presented based on similarities reflected by the runtime behaviors obtained. Fault injection experiments are conducted to verify the prediction measurement by utilizing software faults of Orthogonal Defect Classification to inject and MiBench programs to perform. Over 15 000+ times of fault injection by considering type and location of fault were conducted into each program in order to quantify and analyze the sensitivity on first-error and similarity on failure accordingly. The results show that programs having particular structural features (e.g. massive operations with regards to calculations, nested function-calls, case structures and loop structures, etc.) are well characterized toward first-error behaviors by extracting fine-grained states. Similarities on first-error sensitivity can be represented better for these programs as well. Same trend is seen on failure behavior and its prediction measurement.