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Vogelsang, Andreas; Hartig, Kerstin; Pudlitz, Florian; Schlutter, Aaron; Winkler, Jonas (2019): Supporting
the Development of Cyber-Physical Systems with Natural Language Processing: A Report. Joint
Proceedings of REFSQ-2019 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track.
(CEUR workshop proceedings ; 2376). Aachen: RWTH.
Andreas Vogelsang, Kerstin Hartig, Florian Pudlitz, Aaron Schlutter,
Jonas Winkler
Supporting the Development of Cyber-
Physical Systems with Natural Language
Processin
g

: A Report
Published version Conference paper |

Supp orting the Dev elopmen t of Cyb er-Ph ysical Systems
with Natural Language Pro cessing: A Rep ort
Andreas V ogelsang, Kerstin Hartig, Florian Pudlitz, Aaron Sc hlutter, Jonas Winkler
Automated Systems Engineering T ec hnologies (ASET)
T ec hnisc he Univ ersit¨ at Berlin, German y
{ firstname.lastname } @tu-b erlin.de
Abstract
Soft w are has b ecome the driving force for inn o v ations in an y tec hni c al
system that observ es the en vironmen t with differen t s ensors and influ-
ence it b y c on trolling a n um b er of actuators; no w ada ys called Cyb er-
Ph ysical System (CP S). The dev elopmen t of s uc h system s is inheren tly
in ter-disciplinary and often con tains a n um b er of indep enden t subsys-
tems. Due to this div ersit y , the ma jorit y of dev elopmen t information
is expressed in natural language artifacts of all kinds. In this pap er,
w e rep ort on r e cen t results that our group has dev elop ed to supp ort
engineers of CPSs in w orking with th e large amoun t of information e x -
pressed in natural language. W e co v er the topics of automatic kno wl-
edge e xtr ac ti on, exp e r t systems, and automatic requiremen t s classifi-
cation. F urthermore, w e en vision that natural langu age pro cessing will
b e a k ey comp onen t to connect requiremen ts with sim ulation mo dels
and to explain to ol-based decisions. W e see b oth areas as promising
for supp orting engineers of CPSs in the future.
1 T eam Ov erview and Application Domain
The Automated Systems Engineering T ec hnologies (ASET) group at the T ec hnical Univ ersit y of Berlin is re-
searc hing and dev eloping tec hnologies to supp ort system engineers and automate time-consuming or error-prone
tasks and pro cess steps. With our researc h, w e aim at the dev elopmen t of soft w are-in tensiv e systems that con-
stan tly obse r v e their en vir o n m en t with differen t sensors and try to influence the en vironmen t in a desired w a y
b y con trolling a n um b er of actuators. Si nce soft w are is b ecoming the most imp or tan t and most critical p art of
these system s, they are no w often called Cyb e r - P h ysical Systems (CPS) [Lee08].
Although soft w are is b ecoming most critical f or CPSs, their dev elopmen t is inheren tly in ter-disciplinary in
terms of the in v olv ed application domains (e.g., smart mobilit y) and the i n v olv ed engineering disciplines (e.g.,
mec hanics, electronics, and soft w ar e ). Due to this div ersit y , the ma jori t y of dev elopmen t inf orma t ion is expressed
in natural l a n guage b ecause NL can b e read and un dersto o d b y engineers and stak eholders indep enden t of
their bac kgrou nd kno wledge. In addition, the dev elopmen t of CPSs is driv en b y strong safet y and securit y
constrain ts b ecause most of the times, h umans or ph ysical assets are impacted b y the b eha vior of a CPS. CPS
relev an t dev elopmen t information expressed in natural language do es not only include requiremen ts but also
safet y analyses and assessmen ts, arc hitectural descriptions, test cases, and man y more. Dev elopmen t information
is often spread o v er h undreds of do cumen ts with thousands of single en tries. F or example, the sp ecification
Copyright c
 2019 by the p ap er’s authors. Copying p ermitte d for private and ac ademic purp oses.

rep ository of a telematics system of a mo dern automotiv e system that we are analyzing con tains 28,867 do cumen ts
with 2,423,624 en tries. On the other hand, most of the engineering tasks for CPS are p erformed man ually b y
exp erts who mak e hea vy use of their exp erience and domain exp ertise. These exp erts must be supp orted to cop e
with the amoun t and ric hness of information expressed in natural language.
W e try to tackle these c hallenges in our group by dev eloping three areas of comp etence: Artificial In telli-
gence for Systems Engineering, Mo del-based Engineering, and V alidation b y Sim ulation. W e do researc h in an
application-orien ted manner and test our tec hnologies con tinuously in practice. 1
2 P ast and Curren t Researc h on NLP for CPS Dev elopmen t
W e use NLP techniques to automatically extract specific information from large corp ora of textual do cumen ts,
dev elop exp ert systems that can b e used to retriev e answ ers to sp ecific queries, and to classify information in
textual do cumen ts automatically .
2.1 Automatic Kno wledge Extraction
Engineers of CPSs are c hallenged b y comprehending the concepts men tioned in a requiremen t b ecause coheren t
information is spread o v er sev eral requiremen ts do cumen ts. The reasons are that single do cumen ts often only
co v er the view of one discipline (e.g., mechanics or soft w are) or that the mentioned concepts strongly depend on
other parts of the system that are describ ed in another do cumen t (cf. [VF13]).
W e hav e developed a natural language pro cessing pip eline to transform a set of heterogeneous natural language
requiremen ts from differen t do cumen ts in to a kno wledge representation graph [SV18]. The graph pro vides an
orthogonal view on to the concepts and relations written in the requiremen ts. In a first v alidation of the approac h,
w e applied it to t w o separate requiremen ts do cumen ts including more than 7,000 requiremen ts from industrial
systems (see Figure 1). As the first requiremen ts do cumen t included sev eral subsystems, w e were able to analyze
whic h concept descriptions are distributed o v er subsystems and where those subsystems had intersections to eac h
other (see Figure 1a).
(a) Exterior ligh ting and adaptiv e cruise con trol (b) Charging system for electric v ehicles
Figure 1: Knowledge represen tation graphs extracted from tw o requiremen ts do cumen ts
1 https://aset.tu- berlin.de

A second area that w e ha v e w orked on is the extraction of terms that should be defined and clarified in an
in ter-disciplinary pro ject (i.e., creating a glossary). Creating glossaries for large corp ora of textual do cumen ts
is imp ortan t for creating a shared understanding b et w een all engineers and for unco v ering p oten tial sources of
am biguit y (cf. [FEG18]). Ho w ev er, creating glossaries is also an exp ensiv e task b ecause it is largely man ual.
Automatic glossary term extraction metho ds often fo cus on ac hieving a high recall rate and, therefore, fa v or
linguistic pro cessing for extracting glossary term candidates and neglect the b enefits from reducing the n um b er
of candidates b y statistical filter metho ds [ASBZ17]. Ho w ev er, esp ecially for large datasets, a reduction of the
lik ewise large n um b er of candidates ma y b e crucial.
W e hav e demonstrated how to automatically extract relev ant domain-specific glossary term candidates from
a large b o dy of requiremen ts, the Cro wdRE dataset [GCKV18]. Our h ybrid approac h com bines linguistic pro-
cessing and statistical filtering for extracting and reducing glossary term candidates. In a t w ofold ev aluation,
w e examined the impact of our approac h on the qualit y and quan tit y of extracted terms. W e show ed that a
substan tial degree of recall can b e ac hiev ed ev en if we applied statistical filters to reduce the n umber of false
p ositiv es. F urthermore, w e adv o cate requiremen ts co v erage as an additional qualit y metric to assess the term
reduction that results from our statistical filters. Results indicate that with a careful com bination of linguistic
and statistical extraction metho ds, a fair balance b et ween later man ual efforts and a high recall rate can b e
ac hiev ed.
2.2 Exp ert Systems
The dev elopmen t of CPSs m ust often adhere to dev elopment standards to ensure certain non-functional properties
(e.g., ISO 26262 for safet y-critical systems in automotiv e). According to the standard, the hazard analysis and
risk assessmen t (HARA) is one of the first safet y activities during the dev elopment of safet y-related systems. In
this analysis, exp erts examine p oten tial malfunctions and their consequences in differen t situations, and sp ecify
safet y goals to reduce risks to an acceptable lev el. Performing HARAs i s a time-consuming and exp ensiv e activity
b ecause it is exp ert-driv en and requires extensiv e exp erience and domain kno wledge. Th us, domain experts would
b enefit from decision supp ort that allo ws the automated reuse of appro v ed kno wledge from previous analyses.
Ho w ev er, automated knowledge reuse is considered a c hallenging task.
W e hav e developed an information retriev al system that represents the results from previous HARAs in
a seman tic net w ork and searches it for useful recommendations during a new HARA b y applying spreading
activ ation algorithms [HK16]. W e use the underlying data mo del of the HARA do cumen t to automatically create
a basic seman tic net w orks from semi-structured HARA do cumen ts. Natural language pro cessing tec hniques help
us to refine the net w orks and extract seman tics from coarse-grained text fragmen ts suc h as description elemen ts.
Our approac h aims at making optimal use of the reuse p oten tial and, therefore, increasing the consistency of
HARAs and the efficiency of their dev elopmen t. In an ev aluation, w e ha v e implemen ted the approach based
on a set of 155 existing HARA do cumen ts. The ev aluation reveals goo d quality of the retriev al results and
indicates, whic h configuration settings are adv antageous. Moreo v er, we sho wed ho w configuration settings can
b e optimized with ev olutionary algorithms, whic h extends the dev elop er’s to ol set.
2.3 Automatic Requiremen ts Classification
In CPS dev elopmen t, requirements are not only used to describe the intended c haracteristics of the envisioned
system but also for a n um b er of managemen t tasks suc h as effort estimation, test planning, or con tract design. F or
these tasks, it is imp ortan t to assess and classify single requiremen ts (e.g., b y priorit y , estimated effort, p oten tial
v erification metho d, etc.) In single sp ecifications from the automotiv e domain, we ha ve seen up to 6,048 attributes
with partly more than 100 differen t attribute en tries, whic h where used to annotate requiremen ts in do cumen ts.
W e hav e developed an automatic classification approac h for textual requirements that can be used to supp ort
qualit y assurance. The approac h uses w ord em b eddings to enco de texts and con v olutional neural net w orks
to assign mem b ership v alues to predefined classes [WV16]. After talking to engineers, w e ha v e instan tiated the
approac h for imp ortan t attributes. One example is the classification of textual en tries in to the classes r e quir ement
and information . While requiremen ts are legally binding, information entries con tain additional conten t such as
explanations, summaries, or figures. Our approac h is able to detect errors in this attribute with a recall of 0.95
and a precision of 0.30.

3 F uture Researc h on NLP for CPS Dev elopmen t
W e envision that natural language processing will b e a key component to connect requiremen ts with simulation
mo dels and to explain to ol-based decisions. W e see b oth areas as promising for supp orting engineers of CPSs in
the future.
3.1 Connecting NL Requiremen ts and Sim ulation
CPSs are complex b ecause they are often assem bled from a n um b er of systems that in teract indep enden tly to
some degree. In suc h a context, formal reasoning ab out resulting system b eha vior is hard or even impossible.
Sim ulation is often a b etter alternativ e to explore the complex in terpla y of systems. Ho w ev er, currently , sim ula-
tion in practice is either used in the v ery early stages for feasibilit y studies or in the very late stages to test the
implemen ted system. Requirements engineers do not profit from sim ulation results b ecause the sim ulations are
not connected to the requiremen ts in the sp ecifications.
W e aim at closing this gap b y giving requirements engineers the possibility to relate natural language re-
quiremen ts with observ able ev en ts in sim ulators. As a result, the requirements engineer receiv es information
that annotate the requiremen ts with results from m ultiple sim ulation runs. W e present a first protot yp e of
this approac h in this y ear’s REFSQ conference [PV19]. The c hallenge is to mak e the mapping pro cess as easy
and con v enien t as p ossible for the requiremen ts engineer suc h that the effort pa ys off for him or her. W e aim
at using NLP to supp ort this pro cess (e.g., b y giving recommendations based on similarit y measures b et w een
requiremen ts and descriptions of sim ulation ev ents).
3.2 Explainabilit y
In man y cases, the purp ose of addressing RE tasks with NLP tec hniques is to supp ort the h uman analyst and
not completely replace him or her. Therefore, it is b ecoming more and more imp ortan t that to ol results go
along with explanations of the results. Sometimes, the explanation is ev en more helpful than the actual result.
Ho w ev er, esp ecially with the use of data-driv en tec hnologies suc h as mac hine learning, it is challenging to explain
to ol decisions.
W e try to emphasize the imp ortance of explainabilit y and search for solutions in this field. One example
is the automatic requiremen ts classification to ol that w e already in tro duced in the previous section. T o mak e
the decisions of the to ol explainable, w e ha v e dev elop ed a mec hanism that traces bac k the decision through the
neural net and highligh ts fragmen ts in the initial text that influenced the to ol to mak e its decision [WV17]. As
sho wn in Figure 2, it app ears that the w ord “m ust” is a strong indicator for a requirement, whereas the w ord
“required” is a strong indicator for an information elemen t. While the first is not v ery surprising, the latter
could indicate that information elemen ts often carry rationales (wh y something is r e quir e d ).
Figure 2: Automatic Classification of textual sp ecification ob jects into classes r e quir ement and information .
Another example in whic h w e lo ok ed for explainabilit y is in the recommendations from exp ert system. In
Section 2.2, w e in tro duced our exp ert system for hazard and risk analysis. In this approac h, w e used spr e ading
activation as a tec hnique to extract relev an t concepts for a certain query . Spreading activ ation is a w ell-kno wn
seman tic searc h tec hnique to determine the relev ance of no des in a seman tic net w ork. When used for decision
supp ort, meaningful explanations of semantic searc h results are crucial for the user’s acceptance and trust.
Therefore, we ha v e developed an approach that exploits the so-called spread graph, a sp ecific data structure
that comprises the spreading progress data [MH16]. W e ha ve sho wn how to retriev e the most relev an t parts of
a net w ork b y minimization and extraction techniques and form ulate meaningful explanations.
4 Conclusions
In this rep ort, w e present past w ork and future researc h directions in the area of natural language pro cessing
in the Automated Systems Engineering T echnologies (ASE T) group at the T ec hnical Univ ersity of Berlin. With

our researc h, w e mainly target the dev elopment of cyber-physical systems (CPS). W e argue that the ma jorit y of
dev elopmen t information for CPSs is expressed in natural language due to the div ersit y in in v olved application
domains and engineering disciplines. W e ha ve w ork ed on using NLP techniques to extract specific information
from large corp ora of textual do cumen ts automatically , dev elop exp ert systems that can b e used to retriev e
answ ers to sp ecific queries, and to classify information in textual do cumen ts automatically . W e en vision that
natural language pro cessing will b e a k ey comp onent to connect requiremen ts with sim ulation mo dels and to
explain to ol-based decisions. W e see b oth areas as promising for supp orting engineers of CPSs in the future.
References
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