Analysis and Prediction of Mandelbugs in an Industrial Software System

Abstract

Mandelbugs are faults that are triggered by complex conditions, such as interaction with hardware and other software, and timing or ordering of events. These faults are considerably difficult to detect with traditional testing techniques, since it can be challenging to control their complex triggering conditions in a testing environment. Therefore, it is necessary to adopt specific verification and/or fault-tolerance strategies for dealing with them in a cost-effective way. In this paper, we investigate how to predict the location of Mandelbugs in complex software systems, in order to focus V&V activities and fault tolerance mechanisms in those modules where Mandelbugs are most likely present. In the context of an industrial complex software system, we em- pirically analyze Mandelbugs, and investigate an approach for Mandelbug prediction based on a set of novel software complexity metrics. Results show that Mandelbugs account for a noticeable share of faults, and that the proposed approach can predict Mandelbug-prone modules with greater accuracy than the sole adoption of traditional software metrics and prediction algorithms

How bad can a bug get? an empirical analysis of software failures in the OpenStack cloud computing platform

“…Since software bugs are human mistakes in the source code, the traditional fault-tolerance strategies for hardware and network faults often do not apply. For example, if a service is broken because of a regression bug, then retrying to execute the service API with the same inputs would result again in a failure; a retrial would only succeed in the case that the software bug is triggered by a transient condition, such as a race condition [6,21,22]. If recovery is not possible, the failed operation must be necessarily aborted and the user should be notified [43,47], so that the failure can be handled at a higher level of the business logic.…”

Defect analysis in mission-critical software systems: a detailed investigation

“…Although the testing phase is not the final one, and more bugs of this type are expected to surface in later stages, our conjecture is confirmed by the high percentage of not tried or not available cases. Considering their high impact at operational time , this aspect should be improved. On single CSCIs, we observed again no significant correlation amongst the proportion of not always reproducible defects and any of the testing metrics.…”

Enhanced Binary Moth Flame Optimization as a Feature Selection Algorithm to Predict Software Fault Prediction

“…ML algorithms show promising performance in solving SFP problem. Several algorithms are used such as Naive Bayes (NB) [12], Multilayer Perceptron (MLP) [13], Case-based Reasoning (CR) [14], Artificial Neural Networks [15], Deep learning methods [1], Support Vector Machine (SVM) [16], Bayesian Networks (BN) [17], Decision Trees (DT) [18], Multinomial Logistic Regression (MLR) [13] and Logistic Regression (LR) [17]. Several public bechmarck datasets are available online, and researchers employed their proposed algorithms and compare the obtained results with the literature.…”

“…The authors applied SVM and NB machine learning algorithms, and NB results outperform SVM. Carrozza et al [13] examine five ML algorithms (i.e., MLR, BN, NB, SVM, abd DT) over several datasets from NASA repository. The performance of MLP and SVM outperform other algorithms.…”