Perform Fault Localization Research Articles

A Word Embedding Model for Fault Localization using Bug and Software Change Repositories

Software developed and then deployed in a real world environment is inevitable to exhibit some undesirable behavior. Therefore, developers need to provide maintenance facilities to enable the bugs causing the undesirable behavior to be fixed. However, prior to fixing the bug, the suspicious part of the code needs to be identified. For this purpose, they usually perform fault localization. This can be done manually as well as automatically. Several techniques exist in the literature for fault localization. However, most of them are static based techniques because they do not depend on a specific programming language along with the possibility to work on underdeveloped software and some other benefits. These techniques are largely based on lexical matching of terms which leads to mismatch of terms, large precision value because of limited vocabulary of a programming language and some techniques consider the semantics but it is computationally expensive to localize faults through this. In this paper we have proposed a fault localization technique which is based on the machine learning concept of word embedding. Our proposed approach aims at looking at the relatedness between the bug terms and source code artifact. We mined the bug repositories and software change repositories to train the word embedding model on the mined repositories data. On the arrival of a new bug, the cluster of the bugs from the model is searched and the files from the software change repositories are retrieved which are used for fixing those bugs. We have compared the results of our approach with the latest technique proposed in year 2018 Pointwise Mutual Information (PMI) and Normalized Google Distance (NGD) which consider the context and also with existing lexical techniques Vector Space Model (VSM) and the semantic based method Latent Semantic Indexing (LSI). We have used the benchmark dataset “MoreBugs” which has been widely used in this domain. The results show that our approach outperforms other techniques.

Automated Support for Reproducing and Debugging Field Failures

As confirmed by a recent survey conducted among developers of the Apache, Eclipse, and Mozilla projects, two extremely challenging tasks during maintenance are reproducing and debugging field failures—failures that occur on user machines after release. To help developers with these tasks, in this article we present an overall approach that comprises two different techniques: B ug R edux and F 3 . B ug R edux is a general technique for reproducing field failures that collects dynamic data about failing executions in the field and uses this data to synthesize executions that mimic the observed field failures. F 3 leverages the executions generated by B ug R edux to perform automated debugging using a set of suitably optimized fault-localization techniques. To assess the usefulness of our approach, we performed an empirical evaluation of the approach on a set of real-world programs and field failures. The results of our evaluation are promising in that, for all the failures considered, our approach was able to (1) synthesize failing executions that mimicked the observed field failures, (2) synthesize passing executions similar to the failing ones, and (3) use the synthesized executions to successfully perform fault localization with accurate results.

Multidomain Diagnosis of End-to-End Service Failures in Hierarchically Routed Networks

Probabilistic inference was shown effective in the nondeterministic diagnosis of end-to-end service failures when applied in a centralized management system where the manager possesses a global knowledge of the system structure and state. Since many networks are organized into multiple administrative domains that may be unable to share configuration and state information, these centralized techniques are not applicable to them. This paper proposes a fault localization technique suitable for multidomain networks with hierarchical routing. The proposed technique divides the computational effort and system knowledge among multiple, hierarchically organized managers. Each manager performs fault localization in the domain it manages and requires only the knowledge of its own domain. We show through simulation that the proposed approach not only improves the feasibility of fault localization in multidomain networks, but also increases the effectiveness of probabilistic diagnosis and makes it realizable in networks of considerable size

Probabilistic Fault Localization in Communication Systems Using Belief Networks

We apply Bayesian reasoning techniques to perform fault localization in complex communication systems while using dynamic, ambiguous, uncertain, or incorrect information about the system structure and state. We introduce adaptations of two Bayesian reasoning techniques for polytrees, iterative belief updating, and iterative most probable explanation. We show that these approximate schemes can be applied to belief networks of arbitrary shape and overcome the inherent exponential complexity associated with exact Bayesian reasoning. We show through simulation that our approximate schemes are almost optimally accurate, can identify multiple simultaneous faults in an event driven manner, and incorporate both positive and negative information into the reasoning process. We show that fault localization through iterative belief updating is resilient to noise in the observed symptoms and prove that Bayesian reasoning can now be used in practice to provide effective fault localization.