Investigating Fault Localization Techniques from Other Disciplines

for Software Engineering

Arp

ad Besz

edes

Department of Software Engineering, University of Szeged,

Arp

ad t

er 2., H-6720 Szeged, Hungary

Keywords:

Faults, Defects, Fault Localization, Software Fault Localization, Literature Review, Interdisciplinary Aspects.

Abstract:

In many different engineering ﬁelds, fault localization means narrowing down the cause of a failure to a small

number of suspicious components of the system. This activity is an important concern in many areas, and there

have been a large number of techniques proposed to aid this activity. Some of the basic ideas used are common

to different ﬁelds, but generally quite diverse approaches are applied. Our long-term goal with the presented

research is to identify potential techniques from non-software domains that have not yet been fully leveraged

to software faults, and investigate their applicability and adaptation to our ﬁeld. We performed an analysis of

related literature, not limiting the search to any speciﬁc engineering ﬁeld, with the aim to ﬁnd solutions in non-

software areas that could be most successfully adapted to software fault localization. We found out that few

areas have signiﬁcant literature in the topic that are good candidates for adaptation (computer networks, for

instance), and that although some classes of methods are less suitable, there are useful ideas in almost all ﬁelds

that could potentially be reused. As an example of potential novel techniques for software fault localization,

we present three concrete techniques from other ﬁelds and how they could potentially be adapted.

1 INTRODUCTION

Failures in complex systems can cause damage to the

environment, people’s health and lives, or the opera-

tion of businesses and governments. Hence, possible

underlying faults in the respective engineering disci-

plines are a high priority concern.

These complex systems may be mechanical, elec-

trical, software-driven, or any combination thereof,

but there is one common subtopic, the central theme

of this article, fault localization. In a very general

sense, fault localization in complex (software and

non-software) systems means identifying components

(parts, modules, software code parts, etc.) of the sys-

tem that are responsible for a speciﬁc observed fail-

ure or set of failures. Note, that fault localization is

closely related to the notion of fault detection, the

main difference being that the latter merely shows that

there is a fault somewhere in the system but does not

tell where.

Due to the importance of fault localization, a num-

ber of different discipline- and domain-speciﬁc so-

lutions have been proposed to aid this process in an

In related literature, there is often a confusion about

these terms and their exact meaning might be different ac-

cross various disciplines.

automatic or semi-automatic manner. In some situ-

ations, fault detection and localization are not sepa-

rately handled, but our goal with the present work was

to concentrate on the fault localization aspects only.

In this work, our main concern are software faults.

In particular, our aim is to ﬁnd out whether there are

techniques used in non-software domains that have

not yet been considered for software faults but could

be adapted to the same. We deal with (semi)automatic

fault localization techniques from various engineering

disciplines, and start with an interdisciplinary analy-

sis of the topic. To this end, we performed a system-

atic literature review with a very speciﬁc goal, to dis-

cover their applicability to software fault localization.

We then elaborate on the possible enhancements to

existing techniques or devising novel approaches that

were successful in other disciplines but has not yet

been considered for software.

In any of the mentioned engineering areas, sys-

tems tend to be large and complex, and they are often

connected to each other, forming even more complex

systems-of-systems. Consequently, it may be very

difﬁcult to localize the origin (root cause) of occur-

ring failures. Hence, various ﬁelds have developed

approaches to automate the fault localization process.

Naturally, each ﬁeld deals with its peculiarities and

270

Beszédes, Á.

Investigating Fault Localization Techniques from Other Disciplines for Software Engineering.

DOI: 10.5220/0007928502700277

In Proceedings of the 14th International Conference on Software Technologies (ICSOFT 2019), pages 270-277

ISBN: 978-989-758-379-7

many of the techniques are domain-dependent, yet we

found out that there are some similarities across disci-

plines. Furthermore, some of the methods are generic

and could be applied, theoretically, to any engineering

ﬁeld and fault localization problem.

Software fault localization has a large literature

covering many different subtopics; see (Wong et al.,

2016; Parmar and Patel, 2016). However, other ar-

eas, for instance, aerospace or electronics, have much

longer histories and hence might provide ideas for ad-

vancing the software ﬁeld. Next, a lot of research has

been performed to design effective software fault lo-

calization algorithms and propose their use in differ-

ent phases of the software process, most notably de-

bugging. But, related research suggests that the prac-

tical applicability of research results in this area is still

limited (Kochhar et al., 2016), and further research

is needed to achieve more widespread use of auto-

matic software fault localization by practitioners. We

also noticed that recent results in this area are able to

only marginally improve the effectiveness of previous

techniques because it is increasingly difﬁcult to make

signiﬁcant and highly novel contributions.

Hence the motivation for the present work; to in-

vestigate other engineering ﬁelds and ﬁnd out if they

employ techniques that could be adapted to software

and thus advance this ﬁeld. We found that in some

cases there are barriers to the adoption of such tech-

niques due to the fundamental differences in these

(software and non-software) systems. Some of them

arise from the fact that software systems are much

more intangible, but also there are notable differences

in how they are described and handled, e.g., types of

behavioral models used to describe the expected be-

havior of the systems. In other cases, however, the

techniques or some underlying ideas could be suc-

cessfully adapted to software.

In previous work (Besz

edes, 2019), we presented

details of an interdisciplinary survey of fault local-

ization techniques to aid software engineering. After

providing the necessary background information and

detailed assessment criteria, we presented the catego-

rization of the identiﬁed methods according to several

major ﬁelds. Also, a set of possible relationships be-

tween the areas has been presented. In the present

work, we introduce three concrete examples of how

techniques successfully used in other ﬁelds could po-

tentially be adapted to software faults. Our aim with

this approach is to illustrate the relevance of this re-

search, but further details and more investigation are

needed to elaborate on the actual implementability of

the methods.

The paper is organized as follows. Section 2 de-

scribes the approach we used for the literature sur-

vey. In Section 3, we present the results of the liter-

ature analysis with our initial evaluation and example

adoptable techniques. Finally, Section 4 concludes.

2 LITERATURE SURVEY

In this section, we brieﬂy overview the process we

followed in reviewing fault localization literature in

various engineering ﬁelds.

In the ﬁrst phase, we identiﬁed the most relevant

papers with the help of the google, google scholar,

ResearchGate, Mendeley and Scopus systems. We

used a combination of generic search terms like “fault

localization” and speciﬁc keywords that we expected

to be relevant for our search: networks, electron-

ics, engineering, operations, systems, etc. We also

applied different variations and synonyms to these

terms, which included localizing faults, failure diag-

nosis, problem diagnosis, error localization, and sim-

ilar. This way, we were able to identify related works

in many different engineering ﬁelds.

We restricted the search results to full-text scien-

tiﬁc publications. We aimed at limiting the results

to publications that appeared in peer-reviewed jour-

nals or conferences, however there were few excep-

tions such as doctoral theses and technical reports.

The next ﬁltering we applied was to limit the list

to papers that correspond to some of the following

categories: software-related, generic algorithms, and

methods from loosely related engineering ﬁelds. For

example, pure mathematical methods and approaches

in non-related scientiﬁc branches like biomedicine,

navigation, linguistics or other, were removed.

In addition to the repository and proceeding

searches, we also performed lightweight snow-

balling (Wohlin, 2014) to mitigate the risk of omit-

ting relevant literature. Finally, we consolidated the

results by organizing the works by speciﬁc research

groups or authors and concentrating on a few relevant

reports by the same team.

We then performed an initial classiﬁcation of the

papers based on what fundamental area they belong

to. We used a simple classiﬁcation in this respect:

software, networking, other engineering and vari-

ous/generic. There exists a large amount of publi-

cations that deal with fault localization in computer

networks, hence we established a separate category

for this area. The other engineering category includes

all methods that belong to a speciﬁc engineering ﬁeld

other than software or networks. Finally, there are

some approaches that are not limited to any speciﬁc

ﬁeld (although some of them include one or more ex-

ample applications); in this sense, they are generic.

Investigating Fault Localization Techniques from Other Disciplines for Software Engineering

271

We used the same category to denote methods belong-

ing to some other various ﬁelds.

Finally, we extended the classiﬁcation using other

criteria which might be additionally useful to deter-

mine the methods’ usability in the software domain.

These criteria included:

• The basic approach on which the method is

based on: machine learning, statistical analysis,

entropy-based, etc.

• Single or multiple faults are handled.

• Basic type of data the method relies on.

• The need for a behavior model.

• Other properties of the related studies such as kind

of empirical measurements, size of data sets, and

availability of data or implementation.

Details about these criteria and the analysis outcomes

are omitted from this paper due to space consid-

erations, but all the data are available in previous

work (Besz

edes, 2019).

3 ANALYSIS AND PROPOSED

NEW TECHNIQUES

Our initial ﬁndings indicate that it is not easy to pin-

point only a few candidate methods, rather many of

them may provide interesting ideas, even if not the

complete method is adapted.

Below, we provide an overview of the most no-

table techniques we identiﬁed and their initial evalua-

tion for each of the main fundamental areas. Software

is intentionally omitted from this list, as we wanted to

ﬁnd approaches not yet existing in our ﬁeld. For each

of the three relevant categories (Networking, Other

Engineering and Generic Methods), we propose spe-

ciﬁc new software fault localization methods based on

the related approaches. These can serve as examples

of interdisciplinary application of a technique, but this

does not necessarily mean that we cannot beneﬁt from

other, less directly related methods.

3.1 Networking

The main representative literature we identiﬁed in the

networking category are: (Steinder and Sethi, 2004c),

(Alekseev and Sayenko, 2014), (Yu et al., 2010),

(Brodie et al., 2002), (Brodie et al., 2003), (Chao

et al., 1999), (Chen et al., 2004), (Cheng et al., 2010),

(Deng et al., 1993), (Fecko and Steinder, 2001), (Lu

et al., 2013a), (Hood and Ji, 1997), (Kant et al.,

2002), (Kant et al., 2004), (Katzela and Schwartz,

1995), (Kompella et al., 2005), (Lu et al., 2013b),

(Mohamed, 2017), (Natu and Sethi, 2005), (Natu

and Sethi, 2006), (Natu and Sethi, 2007a), (Natu

and Sethi, 2007b), (Rish et al., 2004), (Tang et al.,

2005), (Traczyk, 2004), (Wang et al., 2012), (Zhang

et al., 2011), (Boubour et al., 1997), (Aghasaryan

et al., 1997), (Aghasaryan et al., 1998), (Steinder

and Sethi, 2004a), (Steinder and Sethi, 2004b), (Ben-

Haim, 1980).

A common element of network fault localization

is the use of probabilistic approaches (such as condi-

tional probabilities and Bayes networks) (Chao et al.,

1999; Tang et al., 2005; Steinder and Sethi, 2004b),

and machine learning (Chen et al., 2004; Deng et al.,

1993; Hood and Ji, 1997), which could probably be

adapted to software faults.

The probing method in computer networks,

e. g. (Steinder and Sethi, 2004c), is an almost direct

analogy to software fault localization (a probe is a

program that executes on a particular network node

and sends commands or transactions to the other ele-

ments of the network, and the responses are observed

and their various properties are measured). A net-

work node may correspond to a software component,

a probe can be seen as a test case, and the responses

from the network can be identiﬁed as the dynamic be-

havior of the system by executing the test cases.

In this case, the Spectrum-Based Fault Localiza-

tion class of methods (Abreu et al., 2009a; Parmar and

Patel, 2016; Wong et al., 2016) could be naturally ex-

tended using results from networking. Here, the basis

of the algorithms is the so-called program spectrum,

which in its basic form is essentially a binary matrix

consisting of the test cases in its rows and the program

elements in the columns. A 1 in a matrix element rep-

resents that the test case executes the corresponding

code element. Then, based on the test case outcomes,

various statistics are computed for each program el-

ement about the number of failing and passing test

case executions traversing it, and the most suspicious

code elements are reported to the user (many failing

test cases and few passing ones make a code element

more suspicious).

Approaches by Yu et al. (Yu et al., 2010), Brodie

et al. (Brodie et al., 2002; Brodie et al., 2003), Cheng

et al. (Cheng et al., 2010), Natu et al. (Natu and Sethi,

2005; Natu and Sethi, 2006; Natu and Sethi, 2007a;

Natu and Sethi, 2007b) provide various optimizations

to the basic probing approaches, which are good can-

didates for adaptation to (spectrum-based) software

fault localization.

In particular, the following can be an optimized

approach to fault localization based on the results

from publications in the networking ﬁeld. These steps

could be for optimal test suite construction (here, a

ICSOFT 2019 - 14th International Conference on Software Technologies

272

test case corresponds to a probe in networking):

1. Start from an initial test case set, e. g., optimizing

for high code coverage.

2. Determining the diagnosis ability of this set.

This can be based on some property using en-

tropy or some other measurement. Similar results

are already available in software fault localiza-

tion (Perez et al., 2017; Perez and Abreu, 2018).

3. Finding the minimal set of test cases, based on

some heuristics such as greedy search.

Based on the mentioned works, the program spec-

tra (coverage matrix) can be extended to a Bayesian

network that encodes probabilistic dependencies be-

tween the possible faults (causes) and the test out-

comes (symptoms) (Brodie et al., 2002).

3.2 Other Engineering Fields

We identiﬁed the engineering ﬁelds of aerospace and

(power) electronics that could potentially be the most

useful for our purposes, along with a few additional

techniques from speciﬁc disciplines like oil pipelines

and chemistry. For these ﬁelds, our main ﬁndigs are:

aerospace (Adamovits and Pagurek, 1993; Balaban

et al., 2007; W. Dries, 1990), power electronics (Ben-

bouzid et al., 1999; Beschta et al., 1993; Poon,

2015; Tanwani et al., 2011), electronics (Peischl and

Wotawa, 2006), oil pipelines (Digernes, 1980), and

chemistry (Venkatasubramanian et al., 1990).

The most promising novel approach to software

fault localization is to use behavioral software mod-

els against which the actual behavior could be com-

pared to. A behavioral model essentially describes

the expected functionality of the system which might

be expressed using various formalisms, either graphi-

cal or textual. This area is often refferred to as Model-

Based Diagnosis. This concept has not yet been inves-

tigated in depth for our ﬁeld. The closest approach is

Abreu et al.’s work on using Bayesian framework for

software fault localization (Abreu et al., 2009b), how-

ever this technique does not incorporate other compo-

nents of model-based diagnosis overviewed below.

In the aerospace industry, the use of model based

reasoning, a branch of artiﬁcial intelligence, is fre-

quent (Adamovits and Pagurek, 1993; Balaban et al.,

2007; W. Dries, 1990). In power electronics (Poon,

2015; Benbouzid et al., 1999; Beschta et al., 1993),

the methods also frequently utilize various models de-

scribing the system. Although somewhat different,

but some approaches to fault localization in electronic

circuits are using behavioral models combined with

simulation. In addition, in some cases the descrip-

tion of the hardware is done in a similar way to soft-

ware source code (Peischl and Wotawa, 2006), which

makes it possibly more easier to adapt to our ﬁeld.

Other areas we investigated often use simulation and

probabilistic approaches as well (Digernes, 1980), or

machine learning with neural networks (Venkatasub-

ramanian et al., 1990), and in these cases a model of

the system is required as well. Often, advanced con-

cepts are applied in these areas such as Kalman ﬁlters

to increase the accuracy of fault estimates.

A model-based approach to fault localization in

software requires reliable behavioral models that de-

scribe the expected behavior. This is then compared

to the observed data from system under examination.

Models are in some cases already available; espe-

cially, in the case of critical systems. Here, system-

atic and rigorous requirement elicitation, fault mode

and effect analysis, and test design is done. Mod-

els and their languages depend on the domain and

the modeling approach used. Although the reason-

ing components of the system may also include ex-

perimental knowledge, the model can largely be seen

as a black box, which encodes the required behavior

and is determined during early software engineering

phases. For successful model-based diagnosis, mod-

els need to be accurate and independent of the reason-

ing structure. Completeness is also important, but the

faulty behaviour is generally little-known. Models are

often encoded in the form of a simulation.

Once the model is available, model-based ap-

proach to fault localization in software would follow

the usual approach in model-based diagnosis: given

observations of the system, the system is simulated

using the model, and the observations actually made

are compared to the observations predicted by the

simulation. The following speciﬁc subtasks would

be required in general: data acquisition (producing

dynamic operational data from program executions,

which can be performed using existing approaches

such as proﬁling, logging and instrumentation), fault

detection (performing the tests and comparison to the

model), hypothesis generation and pruning (produc-

ing and analyzing “infection chains” (Zeller, 2005)

from defects to the symptoms), and hypothesis vali-

dation (through formal model analysis or simulation).

3.3 Generic Methods

In the generic category, we identiﬁed the follow-

ing main techniques: (Isermann, 1984), (Massoum-

nia et al., 1986), (Sv

ard, 2012), (Varga, 2003),

(Lerner and Parr, 2000), (Olivier-Maget et al., 2009),

(Shchekotykhin et al., 2016), (Mehra and Peschon,

1971), (Bouloutas et al., 1993), (Frank, 1996), (Tidriri

et al., 2016), (de Kleer, 2009), (de Kleer and

Investigating Fault Localization Techniques from Other Disciplines for Software Engineering

273

Williams, 1987).

It is worthwhile to note that some techniques we

listed here (e.g. Kleer et al. (de Kleer, 2009; de Kleer

and Williams, 1987)) have their main application in

the mentioned ﬁelds from previous sections such as

electronic circuits and computer networks, and are

based on techniques such as entropy minimization

and probabilistic approach. In particular, Bayesian

framework is the basis for Kleer et al.’s technique,

which has also been reused for software (Abreu et al.,

2009b).

Model-Based Diagnosis, mentioned in the previ-

ous section, aims at ﬁnding the fault of an observed

system based on the knowledge about the system’s ex-

pected behavior, which can be discussed in a generic,

domain-independent manner (Shchekotykhin et al.,

2016; Frank, 1996; de Kleer, 2009; de Kleer and

Williams, 1987). Many of these model the system

as a process, and hence process analysis approaches

are used from control theory (Isermann, 1984; Mas-

soumnia et al., 1986; Sv

ard, 2012; Venkatasubrama-

nian et al., 1990; Mehra and Peschon, 1971).

For software fault localization, we propose to fur-

ther investigate the combination of model based ap-

proaches with data driven methods (which process a

large amount of data from the system’s output and are

based on training data for a correctly working sys-

tem). Tidridi et al. (Tidriri et al., 2016) present an

overview of the approach. We believe this could be a

promising way because a reliable and complete model

is often not available for software, but the operational

data from software executions is easily obtainable,

e. g. from logs. Tidridi et al. also refer other related

works in this area, which can be useful sources for

more information about this set of techniques.

In particular, in a data driven approach, a large

amount of operational data (from logs, etc) are col-

lected in which the fault is ﬁrst detected (whether the

system behavior matches the expected one) and then

classiﬁed (determining the type of the fault). Typical

classiﬁcation types are supervised approaches based

on manually prepared training data (e. g., using Neu-

ral Networks), and unsupervised methods such as var-

ious statistical techniques like Principal Component

Analysis. These techniques can help model-based di-

agnosis is various ways, because in practice it is very

difﬁcult to develop accurate mathematical models for

large and complex software systems. For example,

the primary form of behavior differences to be ob-

served by a model-based approach (see the previous

section) can be generated and learned from a large

amount of dynamic operational data.

4 CONCLUSIONS

The purpose of the presented work was to demon-

strate that there is a lot of potential in interdisciplinary

application of fault localization techniques from sev-

eral different engineering ﬁelds to software faults.

We performed a literature review involving various

ﬁelds, computer networks, aerospace, (power) elec-

tronics, etc., and found that although in many cases

the too large differences in the domains permit the

techniques’ application to our ﬁeld, there are several

ideas that could be leveraged for software.

Preliminary results presented in preceding sec-

tions could provide a starting point for additional

analysis of the techniques, and the selection of the

most promising approaches for further consideration.

We also present three concrete approaches and how

they could be adapted to software fault localization.

Although we performed the literature analysis in a

systematic way, we cannot claim its completeness. In-

deed, we did not dive into the details of any particular

subﬁeld or set of techniques. Instead, we concentrated

on locating the most important approaches from vari-

ous ﬁelds. Nevertheless, we believe that the survey in

its present state is suitable for us to start designing in

detail novel approaches to software fault localization,

and for other readers to obtain a wider view of this

important and diverse topic.

As future work, we will continue searching for

other related techniques, extend the assessment with

additional aspects and evaluate the most promising

approaches in more detail. We will eventually start

implementing the new techniques for software fault

localization and compare their performance to other,

already established techniques in this ﬁeld.

Detailed assessment data of the literature survey

are available from the author.

ACKNOWLEDGEMENTS

Arp

ad Besz

edes was supported by the J

anos Bolyai

Research Scholarship of the Hungarian Academy of

Sciences. The author would like to thank the sup-

porting work of Ott

o E

otv

os and Br

o Ilovai who

were supported by project EFOP-3.6.3-VEKOP-16-

2017-0002, co-funded by the European Social Fund.

Ministry of Human Capacities, Hungary grant 20391-

3/2018/FEKUSTRAT is acknowledged.

ICSOFT 2019 - 14th International Conference on Software Technologies

274

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