Toward a Better Understanding of How to Develop Software Under

Stress – Drafting the Lines for Future Research

Joseph Alexander Brown, Vladimir Ivanov, Alan Rogers,

Giancarlo Succi, Alexander Tormasov and Jooyong Yi

Innopolis University, Universitetskaya St, 1, Innopolis, Respublika Tatarstan, 420500, Russia

Keywords:

Software Development Under Adverse Circumstances, Empirical Software Engineering, Software Quality.

Abstract:

Software is often produced under signiﬁcant time constraints. Our idea is to understand the effects of vari-

ous software development practices on the performance of developers working in stressful environments, and

identify the best operating conditions for software developed under stressful conditions collecting data through

questionnaires, non-invasive software measurement tools that can collect measurable data about software en-

gineers and the software they develop, without intervening their activities, and biophysical sensors and then

try to recreated also in different processes or key development practices such conditions.

1 INTRODUCTION

Software is often produced under signiﬁcant time

constraints. Our idea is to understand the effects of

various software development practices on the per-

formance of developers working in stressful environ-

ments, and identify the best operating conditions for

software developed under stressful conditions. To

achieve this goal, we argue to divide the research in

the following two phases: “in vitro” and “in vivo”.

In the “in vitro” phase, the conditions under which

people operate the best will be identiﬁed and moni-

tored by collecting data through questionnaires, non-

invasive software measurement tools that can collect

measurable data about software engineers and the

software they develop, without intervening their ac-

tivities, and biophysical sensors.

In the “in vivo” phase, the best working conditions

identiﬁed in the earlier “in vitro” phase will be recre-

ated in order to study their effects in various stress-

ful conditions. In this phase, it will also be inves-

tigated the effects of well-known development prac-

tices such as pair programming, test driven develop-

ment, inspection, collective code ownership, constant

integration.

In the next section we brieﬂy survey the state of

the art and related works. Then, in Section 3 we de-

ﬁne the problem statement and speciﬁc research ques-

tions, Finally, in Section 4 we present our view of

the possible solution of the problem and concrete ap-

proach to the novel research agenda.

2 RELATED WORKS

2.1 Software Process Improvement

The work on software process improvement has

spanned decades using various methodologies

(Marino and Succi, 1989; Valerio et al., 1997;

Vernazza et al., 2000), processes (Kivi et al., 2000;

Petrinja et al., 2010; Rossi et al., 2012a; Corral et al.,

2013b; Kov

acs et al., 2004), and devices (Corral

et al., 2011; Corral et al., 2013a) and there is a large

corpus of scientiﬁc studies referring to it as it is

evidenced by the recent literature reviews on the

subject (Khan et al., 2017). The discipline is now

moving to acknowledge speciﬁc aspects of it, like

SMEs working on web-based systems (Sulayman and

Mendes, 2009), process and simulations (Ali et al.,

2014), agile methods (Campanelli and Parreiras,

2015).

Particular relevance is now placed on empiri-

cal evaluations of new approaches (Unterkalmsteiner

et al., 2012; Pedrycz et al., 2015a). The proposed

work moves exactly along these lines, proposing new

approaches to a particularly difﬁcult development

process centered on a clear empirical understanding

of the best conditions under which software develop-

ers and engineers produce their work.

398

Brown, J., Ivanov, V., Rogers, A., Succi, G., Tormasov, A. and Yi, J.

Toward a Better Understanding of How to Develop Software Under Stress – Drafting the Lines for Future Research.

DOI: 10.5220/0006794103980405

In Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2018), pages 398-405

ISBN: 978-989-758-300-1

2.2 Inﬂuence of the State of Mind on the

Quality of Developers

It is a well known fact that the state of mind inﬂu-

ences work and that especially positive feeling tend

to be correlated with high quality work, especially in

knowledge-intensive ﬁelds, as discussed in multiple

research works, like the one of (Amabile, 1996; Sil-

litti et al., 2004; Lyubomirsky et al., 2005; Barsade

and Gibson, 2007; Baas et al., 2008; Janes and Succi,

2012; Di Bella et al., 2013)

The inﬂuence of the state of mind on the qual-

ity of the software being developed has been recog-

nized since the early stages of software engineering.

From the 1950-s there have been studies trying to un-

derstand the psychological proﬁles of developers ac-

knowledging the intrinsic connection that exists be-

tween the state of the mind and the quality of the

code, like the work of Rowan (Rowan, 1957), and the

role of personalities and of interpersonal communica-

tions has been a central part of the agile approaches to

software development as championed by the works of

Cockburn and Highsmith (Cockburn and Highsmith,

2001), Williams and Cockburn (Williams and Cock-

burn, 2003; Fronza et al., 2009; Feldt et al., 2010;

Denning, 2012), and others.

There has also been a signiﬁcant literature ev-

idencing that happiness and positive feelings have

a positive impact on quality and productivity in

the workplace and speciﬁcally promotes creativity

(Brand et al., 2007; Davis, 2009). This is particularly

important in software development, which includes a

high amount of creativity as it has been acknowledged

for many years now in several research work like (Fis-

cher, 1987; Glass et al., 1992; Shaw, 2004; Knobels-

dorf and Romeike, 2008; Lewis et al., 2011).

More recently, there have been studies linking the

speciﬁc concept of well-being and concentration to

the effectiveness in producing quality software. In

2002, Succi et al. have conducted one of the ﬁrst

research endeavours linking speciﬁc software prac-

tices to job satisfaction and low turnover (Succi et al.,

2002), and then creating a model for explaining job

satisfaction and its inﬂuence on quality and produc-

tivity (Pedrycz et al., 2011), exploring how develop-

ers move in their workplace (Corral et al., 2012) and

relating pair programming with developers attention

(Sillitti et al., 2012).

Scientiﬁc research has also explored the speciﬁc

concept of happiness at work, connecting it to high-

quality software artifacts like the works of (Khan

et al., 2011; Graziotin et al., 2013; Graziotin et al.,

2014; Murgia et al., 2014).

In all these studies the main vehicle for collecting

data have been questionnaires and subjective evalua-

tions. Biophysical signals have not been used. Some

research has already performed also using such sig-

nals using suitable devices, and it is concentrated in

mainly three research units.

2.3 Studies of Biometric Sensors to

Evaluate the State of the Mind of

Developers

In this subsection we concentrate the description on

the key studies using biometrics sensors to evaluate

the state of the mind of developers and their relation-

ships with tasks to accomplish. There is not any sig-

niﬁcant research effort on how to develop software

under stress. Fritz at al. obtain metrics that correlate

with software developers performance. In (Z

uger and

Fritz, 2015) they used interruptibility while (M

uller

and Fritz, 2015) used positive and negative emotions

of software developers as metrics of progress in the

change task.

They analyze data from multiple bio-sensors, in-

cluding eye trackers for measuring pupil size and eye

blinks, electroencephalography to determine brain ac-

tivity, electrodermal activity sensors to detect skin-

related activity, and heart-related sensors. They ap-

ply methods of supervised learning (Naive Bayes) to

distinguish levels of these cognitive states.

The limit of their approach is that the devices us-

ing to collect the data were mostly focused on collect-

ing emotions and the data analysis was focused on

ﬁnding correlation between emotions and progress,

which was the core of the study. Monitoring the state

of the mind in depth was not their purpose so that

analysis was not precise, and was also limited be-

cause: (i) the assessment of emotions was performed

subjectively by the participants; (ii) a single channel

electroencephalogram (EEG) device was used, which

may result in an error of up to ﬁfty percent (Maskeli-

unas et al., 2016).

Apel with colleagues study the work of the

brain using very accurate techniques, like the func-

tional magnetic resonance imaging (fMRI). They de-

tected activation speciﬁc Broadmann-areas during

code comprehension (Siegmund et al., 2014).In their

follow up work they investigated the difference be-

tween bottom-up program comprehension and com-

prehension with semantic cues in terms of brain areas

involved (Siegmund et al., 2017).A group led by Heui

Seok Lim uses a full EEG device, like the one pro-

posed in this research. However, they focus mostly on

exploring how the mind of developers evolved from

novice to experts in program comprehension tasks,

and therefore have a completely different focus than

Toward a Better Understanding of How to Develop Software Under Stress – Drafting the Lines for Future Research

399

ours (Lee et al., 2016).

3 PROBLEM STATEMENT

Constant stress leads to physiological disadaptation

with increased fatigability and to burn-out syndrome

with decreased motivation for work, and thus inabil-

ity to perform such important tasks. The main ques-

tion is therefore, how it is possible to develop software

when the stress is there and cannot be eliminated, but

needs to be somehow mitigated to ensure high quality

work. In other terms, the research problem is to ﬁnd

and study the best operating conditions for software

that is developed under major time and psychological

pressure for the developers like, for instance, when

a remotely operated spacecraft is moving to an un-

desired location due to an error in the software, or a

controller of a pipeline is wrongly operating causing

leakages of gas.

Moreover, we will consider the following two sub-

problems: (i) when the stress occurs in a limited pe-

riod of time, like when there is the need to ﬁx a sin-

gle safety-critical error within one working day; (ii)

when the stress spans longer intervals, like when a

safety critical condition arises on a whole system, so

that multiple days or weeks of work are required.

These two scenarios require different approaches,

since on the ﬁrst case, very intense working patterns

can be adopted, taking into account that a compen-

sation might occur in the immediate future after the

stress has occurred, while in the second there should

be a pattern of work ensuring the ability to maintain

the quality and productivity of a team for a longer pe-

riod of time. In detail, speciﬁc research questions that

can be addressed are:

RQ1: what is the effect of stress induced by the work-

conditions on the mind of software developers and en-

gineers and the implication of this stress on the quality

of the software systems being produced,

RQ2: what mind states can be observed in soft-

ware developers and engineers during stressful work-

ing conditions that are associated with either low or

high quality and productivity, and then, speciﬁcally:

(a) what are the typical working conditions when the

stress results in low quality work or in loss of pro-

ductivity, and how these condition can be mapped on

mind states of developers, and (b) what are the de-

tailed software development processes or key individ-

ual processes and products patterns and practices that

are observed to correlate with high quality and the

productivity during critical circumstances and what

are the associated mind states of software developers

and engineers,

RQ3: what software development processes or key

individual processes and products patterns and prac-

tices, or other actions can be elaborated to recreate in

software developers and engineers the mind states that

are typically associated with high quality and produc-

tivity, and how they can be elaborated within speciﬁc

software development environments,

RQ4: what are the quantitative effects of the ap-

plication of such processes and key individual pro-

cesses and products patterns and practices in terms of

productivity and quality of the generated software as

functions of the condition of their use (a mapping be-

tween a working context and a problem to face).

The research questions require a lot of practical

effort and preparation of speciﬁc environments to an-

swer. In the next section we propose an approach to

develop such an environment based on neuroimaging

techniques, non-invasive software measurement tools

and methods for evaluation of individual processes

and products patterns in software engineering.

4 PROPOSED APPROACH

Despite the recent trend of using neuroimaging tech-

niques such as fMRI to understand the mind of de-

velopers, most work has been focused on general un-

derstanding of developers mind in the general con-

text. As mentioned above such work has contributed

to the understanding of developers mind, but it is of-

ten not clear how those general understandings can

be applied to concrete real-world problems in soft-

ware industry. In contrast, our approach focuses on

a speciﬁc and critical context, that is, software devel-

opment under stress-inducing circumstances. Under-

standing of developers mind in this speciﬁc context is

not only scientiﬁcally novel, but also can make prac-

tical impact on software development practices.

While we exploit emerging neuroimaging tech-

niques (in particular, multi-channel EEG), these new

techniques do not directly show which development

methodologies and practices lead to better perfor-

mance. Best development methodologies and prac-

tices can be identiﬁed only after considering not only

neroimages but also other numerous factors related to

developers and the artifacts created by the develop-

ers. Thus, the approach should support understand-

ing not only mental effects of various software devel-

opment practices on the performance of developers

working in stressful environments. The key feature of

the approach is systematic investigation of the prac-

tices from most relevant points.

ENASE 2018 - 13th International Conference on Evaluation of Novel Approaches to Software Engineering

400

4.1 Outline of the Research Agenda

In this position paper we describe our agenda for the

future research in the selected direction. At the ﬁrst

step we will select a family of software development

processes for stressful circumstances. Further, we

will collect key individual processes and products pat-

terns and practices that are particularly useful when

the critical circumstances arise. Next, we develop

a framework for quantitative evaluation of such pro-

cesses and key individual processes and products pat-

terns and practices in terms of: (a) the conditions

when best to use them (working context – problem

to face; development environment; kind of software

being developed), and (b) the results in terms of pro-

ductivity and quality of the generated software.

Finally, it is necessary to develop a set of tools that

can help software engineers practice software devel-

opment processes appropriate for a given context. For

example, when software engineers work in an emer-

gency situation, they will be able to accomplish their

work more effectively and efﬁciently, with the help of

the provided tools. Moreover, a system of integrated

tools based on existing physical devices and software

components and supplemented by a suitable integra-

tion layer and additional analysis techniques to collect

the experimental data and to analyze it, to produce the

results mentioned above.

We propose not only a systematic adoption of non-

invasive measurement techniques (including analysis

of processes and of products - code repositories, issue

tracking data, budgeting information), but we cou-

ple it on one side with more standard data collected

via surveys and on the other to biophysical data col-

lected through suitable wearable devices, like wear-

able EEG, eye tracking devices, etc. This is possi-

ble by our partnership with key software development

organizations which produce software for safety and

business critical applications. We will be able to col-

lect data from a uniquely large set of environments.

Various experimentation are naturally ﬁt into the

proposed research agenda and could be used by other

researchers as the cornerstone for subsequent anal-

yses and also for identifying additional possible in-

terpretations and for proposing other processes and

product patterns and practices.

4.2 Background Research

The proposed agenda will be implemented along the

following lines: (a) data collection, (b) data analysis

and model construction, and (c) model validation and

reﬁnement.

The ﬁrst line refers mostly to the data collection.

The work in this area will start from the idea of non-

invasive data collection recently revised and actual-

ized with the system Innometrics, whose most recent

description will be presented at the 33rd ACM Sym-

posium on Applied Computing (SAC 2018) (Bykov

et al., 2018). The data collection at companies will

also be performed through suitable questionnaires and

surveys, using the best standards in the ﬁeld as can

be applied in software engineering, as described in

(Pedrycz et al., 2011; Ivanov et al., 2016). Additional

data will be collected from full capacity EEG devices,

one instance of which is already in use for feasibility

studies at Innopolis University using the on one side

the recent experience collected in software engineer-

ing (Lee et al., 2016) and on the other the decades

long competence presence in neurosciences.

The second line refers to data analysis and model

construction. As mentioned, the data will be ana-

lyzed using statistics and machine learning. Statis-

tics will be the standard statistical tools, as described

in (Moser et al., 2007), more advanced regression

techniques, as described in (di Bella et al., 2013),

approaches coming from reliability growth models

(Ivanov et al., 2016). In terms of machine learn-

ing, we propose to use simple models based on lo-

gistic regression, support vector machine and ran-

dom indexes (Fronza et al., 2013), techniques based

on neural networks, fuzzy logic, granular computing

(Pedrycz et al., 2015b), etc. For generalization pur-

poses, statistical meta-analysis will be adopted (Djo-

kic et al., 2012).

The third line refers to model validation and re-

ﬁnement. Substantially, we will build a suitable ex-

perimental design and, around it, we will run our re-

peated experimentation with the goal of ensuring va-

lidity and generalizable models, along the lines of the

work done in (Succi et al., 2001; Succi et al., 2003a;

Succi et al., 2003b; Paulson et al., 2004; Rossi et al.,

2006; Janes et al., 2006; Rossi et al., 2012b; Janes

et al., 2013; Coman et al., 2014) and (Russo et al.,

2015).

4.3 Infrastructure

Our infrastructure mainly consists of a non-invasive

metrics collection system integrated with hardware

devices collecting biometrics data. Note that our

metrics collection system will collect various met-

rics encompassing metrics related to biometrics data

of developers, metrics about developer activities per-

formed during software development, metrics about

software development process, and metrics about

software artifacts. Our metrics collection system will

also include software packages for statistical analysis

Toward a Better Understanding of How to Develop Software Under Stress – Drafting the Lines for Future Research

401

that can be used to analyze collected data.

In fact, the metrics collection system opens a new

door to research on various topics for which it is es-

sential to collect credible data about developers and

software artifacts they develop. Regarding our infras-

tructure, we plan apply the system in two major stud-

ies: (1) a holistic metrics collection system that can

collect metrics related to biometrics data of develop-

ers, metrics about developer activities, software de-

velopment process, and software artifacts, and (2) an

initial evaluation of using our holistic metrics collec-

tion system for study of software development under

stress-inducing circumstances.

4.4 User Studies and Experiments with

Students and Developers

With user study with industry partners, we expect to

identify common practices exercised by developers to

deal with stresses in their workplaces. We plan to re-

port new ﬁndings we expect to ﬁnd to major software

engineering conferences and workshops. Note that

such ﬁndings will not only enable our research, but

also can help other researchers investigate the issues

on stresses of developers.

Based on the ﬁndings we obtain from the user

study, we plan to investigate the actual effects of the

practices exercised in the ﬁeld to deal with stresses of

developers. We also investigate the effects of these

practices on performances and productivity of devel-

opers. For the sake of feasibility, we ﬁrst plan to ex-

periment with students of Innopolis University, and,

based on the results we obtain, we also plan to per-

form similar experiments in industry partners.

5 CONCLUSION

Developing software systems is a knowledge inten-

sive task, and as such is heavily inﬂuenced by the

state of mind of developers. It has therefore histor-

ically been claimed that software has to be developed

in a quiet and relaxed environment. However, this is

hardly the case. Software is often produced under sig-

niﬁcant time constraint. Sometimes it even happens

that patches for safety critical systems have to be re-

leased because one of such system is malfunctioning

or not working at all with severe and even fatal con-

sequences for its intended users. Notable examples

for this include the aircraft and transportation indus-

try and the overall energy industry.

The main idea presented in this paper is to un-

derstand the effects of various software development

practices on the performance of developers working

in stressful environments, and identify the best oper-

ating conditions for software developed under stress-

ful conditions. We discussed the possible research

agenda and provide our view on its implementation

with the state of the art technologies and approaches.

ACKNOWLEDGEMENTS

We thank Innopolis University for generously funding

this research.

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