DOKK Library

HLola: a Very Functional Tool for Extensible Stream Runtime Verification

Authors César Sánchez Felipe Gorostiaga

License CC-BY-4.0

                 HLola: a Very Functional Tool for
              Extensible Stream Runtime Verification?
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                      Felipe Gorostiaga1,2,3(B)          and César Sánchez1


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                            1                                                                              at e d
                           IMDEA Software Institute, Madrid, Spain
                        Universidad Politécnica de Madrid, Madrid, Spain
                                  CIFASIS, Rosario, Argentina

         Abstract. We present HLola, an extensible Stream Runtime Verification (SRV)
         tool, that borrows from the functional language Haskell (1) rich types for data
         in events and verdicts; and (2) functional features for parametrization, libraries,
         high-order specification transformations, etc.
         SRV is a formal dynamic analysis technique that generalizes Runtime Verifica-
         tion (RV) algorithms from temporal logics like LTL to stream monitoring, al-
         lowing the computation of verdicts richer than Booleans (quantitative values and
         beyond). The keystone of SRV is the clean separation between temporal depen-
         dencies and data computations. However, in spite of this theoretical separation
         previous engines include hardwired implementations of just a few datatypes, re-
         quiring complex changes in the tool chain to incorporate new data types. Addi-
         tionally, when previous tools implement features like parametrization these are
         implemented in an ad-hoc way. In contrast, HLola is implemented as a Haskell
         embedded DSL, borrowing datatypes and functional aspects from Haskell, re-
         sulting in an extensible engine4 . We illustrate HLola through several examples,
         including a UAV monitoring infrastructure with predictive characteristics that has
         been validated in online runtime verification in real mission planning.

1      Introduction
Runtime Verification [4, 14, 18] is a dynamic technique that studies (1) how to generate
monitors from formal specifications, and (2) algorithms to monitor the system under
analysis, one trace at a time. Early RV specification languages were based on logics
like past LTL [19] adapted to finite traces [5, 10, 15], regular expressions [23], fix-point
logics [1], rule based languages [3], or rewriting [21]. Verdicts and many times observa-
tions in most of these specification logics are restricted to Booleans, often because most
early logics in RV were borrowed from static verification—where decidability is cru-
cial. SRV [9,22] attempts to generalize these monitoring algorithms to richer datatypes,
including in observations and verdicts. SRV offers declarative specifications where off-
set expressions allow accessing streams at different moments in time, including future
instants. Most previous SRV developments [9, 11] and their extensions to event-based
     This work was funded in part by the Madrid Regional Government under project “S2018/TCS-
     4339 (BLOQUES-CM)”, by Spanish National Project “BOSCO (PGC2018-102210-B-100)”.
     The tool is available open-source at
© The Author(s) 2021
J. F. Groote and K. G. Larsen (Eds.): TACAS 2021, LNCS 12652, pp. 349–356, 2021. 18
350     F. Gorostiaga and C. Sánchez

systems [8,11,12,17] focus on efficiently implementing the temporal engine, promising
that new datatypes can be incorporated easily. However, in practice, adding a datatype
requires modifying the parser, the internal representation and the runtime system. Con-
sequently, existing tools only support a limited hardwired collection of datatypes (typi-
cally Booleans and numeric types for quantitative monitoring).
     In this paper we demonstrate the tool HLola, whose core language is Lola [9],
but that enables arbitrary datatypes. HLola is implemented as an embedded DSL in
Haskell. Other RV tools implemented as eDSLs include [2, 13] (in Scala), and [24]
which implements LTL as an eDSL in Haskell. The main theoretical novelty of HLola
is a technique called lift deep embedding, that consists in borrowing types transparently
from Haskell and embedding the resulting language back into Haskell (see [7] for an in-
troduction to HLola with details of the theoretical underpinnings). In fact, most HLola
datatypes were introduced after the temporal engine was completed without requiring
any re-implementation. An eDSL enables higher-order functions to describe transfor-
mations that produce stream declarations from stream declarations, enabling stream
parametrization for free. HLola libraries collect these transformers so new logics like
LTL, MTL, etc with Boolean and quantitative semantics can be implemented in a few
lines (see Section 2). Haskell type-classes enable simplifiers, which can anticipate the
value of an expression without requiring the computation of all its sub-expressions.
Implementing these in previous systems requires to re-invent and implement features
manually (like macro expansions, etc). HLola even allows specifications as data to im-
plement “specifications within specifications” (a feature that allows computing a full
auxiliary specification at every instant, useful in simulation and for nested properties).
This is used in an UAV scenario to implement Kalman filters [16] as monitors that
predict the trajectory of the unmanned aircraft. The output of this monitor is used to
anticipate problems (using another monitor) and take preventive planning actions.

Stream Runtime Verification in a nutshell SRV generalizes monitoring algorithms to
arbitrary data, where datatypes are abstracted using multi-sorted first-order interpreted
signatures (called data theories in the Lola terminology). The signatures are interpreted
in the sense that every functional symbol f used to build terms of a given type is accom-
panied with an evaluation function f (the interpretation) that allows the computation of
values (given values of the arguments). A Lola specification hI, O, Ei consists of (1) a
set of typed input stream variables I, which correspond to the inputs observed by the
monitor; (2) a set of typed output stream variables O which represent the outputs of
the monitor as well as intermediate observations; and (3) defining equations, which as-
sociate every output y ∈ O with a stream expression Ey that describes declaratively
the intended values of y. The set of stream expressions of a given type is built from
constants and function symbols as constructors (as usual), and also from offset expres-
sions of the form s[k, d] where s is a stream variable, k is an integer number and d is
a value of the type of s used as default. For example, altitude[-1,0.0m] repre-
sents the value of stream altitude in the previous step of time, with 0.0m as default
value to be used at the initial instant. Online efficient algorithms can be synthesized for
specifications with (bounded) future accesses [9, 22], where efficiency means that re-
sources (time and space) are independent of the length of the trace and can be calculated
statically. HLola can be efficiently monitored in a trace-length independent sense [7].
                    HLola: a Very Functional Tool for Extensible Stream Runtime Verification            351

2       The HLola Tool

Fig. 1 shows the software architecture of HLola. We start from an HLola specification,
which can borrow datatypes, notation and features from the Haskell language (repre-
sented by the red dashed arrow in Fig. 1). A simple translator processes the specification
and generates code in the Haskell eDSL. The translator does not fully parse the spec
and only preforms simple rewrites, leaving most of the specification unchanged. The
resulting code is combined with the HLola engine (developed in Haskell) and compiled
into a binary in the target platform. A well-known downside of this approach is that
during the second compilation stage, error reports may be rather cryptic. On the other
hand, a Haskell expert can write specifications directly in the embedded DSL, which
still resembles Lola, to finely tune an HLola specification.
     The enhanced capabilities of HLola with respect to Lola (streams as data, stream
type polymorphism and parametric streams) impact the syntax of the language, which
diverges slightly from the syntax of the original Lola. HLola files can either be libraries
or specifications: Libraries include HLola code that define streams and facilities to cre-
ate streams, and must be declared using library <Name> (where <Name> is the name
of the library) on the first line of the HLola file. Specifications first state the format for
input and output events as format JSON or format CSV. Source files then can import
libraries and stream data manipulation facilities (called theories) with the statements
use library <Name> and use theory <Name> respectively. HLola files can also
import arbitrary Haskell libraries using the statement use haskell <Name>, and in-
clude Haskell code directly anywhere within the blocks delimited between #HASKELL
and #ENDOFHASKELL. Specifications then define the input and output streams. An Input
stream is declared by its type and name in a line of the form input <Type> <name>,
just like in the original Lola language. The syntax of <Type> follows the Haskell no-
tation. An Output stream is specified by its type, name and parameters on the left hand
side of =, and its defining expression on the right hand side of =:
 output <TypeConstraints>? <Type> <name> <args>* = <Expr>,
where <TypeConstraints> is an optional set of constraints over the polymorphic
types handled by the stream (expressed in Haskell notation), and <args> is an optional
list of arguments of the form <Type> <name>. We can use define instead of output
to define intermediate streams, whose values are not reported by the monitor but can
be used by other streams. The defining <Expr> of an output stream allows the use of

                          HLola        
                                         • complex datatypes                    Haskell     Binary
                                        • libraries
                                         • functional notation     SRV
                                        • partial application    engine
                                        • high order                       +
       HLola                                                     Embedded         Haskell
                           to                                                                  Binary
    specification                                                  DSL           compiler

                                   Fig. 1. Software Architecture of HLola.
352        F. Gorostiaga and C. Sánchez

let clauses, where blocks, type annotation, do notation, etc. The access to the value
of a stream s at the current instant uses the term s[now] to distinguish it from s, the
stream itself (whose type is stream of values). The offset expression that accesses a
stream s at a shift of i with default value d is written as s[i|d], as in classic Lola. The
symbol ’ is used to lift an object o from the theory as in ’o. We sometimes indicate
the arity of the object o being lifted for clarity or to aid the type inference as in 2’o. To
improve readability, some operators have been overridden by their lifted version, such
as if-then-else.
Libraries. The following HLola file defines a library of Past-LTL operators, called LTL,
as part of the HLola distribution5 .

library LTL
use library Utils
output Bool historically <Stream Bool p> = p[now] && historically p [-1|’True]
output Bool once <Stream Bool p> = p[now] || once p[-1|’False]
output Bool since <Stream Bool p> <Stream Bool q> = q[now] ||
                                               (p[now] && p ‘since‘ q [-1|’False])
output Int nFalses <Stream Bool p> = nFalses p[-1|0] + if p[now] then 0 else 1
output Double percFalses <Stream Bool p> = nFalses p[now] ‘intdiv‘ (instantN[now])

The auxiliary library Utils includes instantN, which stores the current instant num-
ber. Stream historically is parametrized by Boolean stream p. Once instantiated,
historically p will be true until p becomes false for the first time, and will be
false thereafter. This definition uses offsets to define the unrolling, using the constant
value true in the first instant, lifted from Haskell as ’True. This library also contains
quantitative operators like nFalses, that counts the total number of falsifications up to
an instant, and percFalses that calculates the ratio of falsifications. A similar library
for MTL includes the parametrized definition of ϕ U(a,b) ψ:
output Bool until <(Int,Int) (a,b)> <Stream Bool phi> <Stream Bool psi> = from a
  where from a | a == b    = psi[a|’False]
               | otherwise = psi[a|’False] || (phi[a|’True] && from (a+1))

Here the parametrized stream until takes the interval (a, b) and the streams ϕ and ψ
as parameters. Similarly, the library for Quantitative MTL introduces a parametrized
stream to calculate the arithmetic mean of the last k values of a given stream:

output Double meanLast <Int k> <Stream Double str> = numr / denom
  where denom=1’fromIntegral (2’min ’k (instantN[now])) ; numr=sumLast k str [now]

which takes as parameters the window size k and the stream str. The denominator is
the minimum of k and instantN, converted to Double. The numerator is the sum of
the last k values in str. Polymorphosim allows us to generalize this definition to any
Haskell type as long as it is Fractional, Equalizable and Streamable, using the following
stream signature instead (and the same expression):

output (Eq a, Fractional a, Streamable a) => a meanLast <Int k> <Stream a str>

     All libraries, definitions and examples are available open-source in the GitHub repository and
             HLola: a Very Functional Tool for Extensible Stream Runtime Verification   353

3     Example Specifications
In this section we show a collection of HLola specifications to demonstrate the capabil-
ities of HLola to define stream based monitors.
Temporal Logics. HLola allows us to easily define, in a declarative way, many specifi-
cations written in temporal logic. The HLola distribution contains many LTL examples,
including a sender/receiver model from [6], and other temporal logics. Consider the fol-
lowing MTL property from [20]: (alarm → ([0,10] allClear ∨ [10,10] shutdown)),
which includes deadlines between environment events and the corresponding system
responses, stating that that an alarm is followed by a shutdown event in exactly 10 time
units unless allClear is received. This is defined in HLola as follows:
format JSON
use library MTL
data Event = Alarm | AllClear | ShutDown deriving (Generic,Read,FromJSON,Eq)
input Event event
define Bool allClear = event [now] === ’AllClear
define Bool shutdown = event [now] === ’Shutdown
define Bool alarm       = event [now] === ’Alarm
output Bool property = alarm [now] ‘implies‘ (willClear[now] || willShutdown[now])
     where willClear    = eventually (0,10) allClear
           willShutdown = eventually (10,10) shutdown

Pinescript example. TradingView is an online charting platform for stock exchange,
which offers the Pinescript language to query stock time series. Pinescript queries are
then run in the company’s servers. We have implemented the indicators of Pinescript
in HLola as a library, and we have implementated a trading strategy6 using the HLola
Pinescript library. Compared to Pinescript, HLola offers formal semantics, runtime re-
source guarantees (time and space) and is much more expressive, for example allowing
relational queries that involve multiple stocks (their averages, etc).

UAV specifications. We have used HLola also for the online monitoring of several
properties of UAVs missions. For example: (1) That the UAV does not fly over for-
bidden regions, and (2) that the UAV is in good position when it takes a picture. The
input streams of these two specifications consist of the state of the UAV at every in-
stant and the onboard camera events to detect when a picture is being captured. This
specification imports geometric facilities from theory Geometry2D, and Haskell li-
braries Data.Maybe and Data.List. It then defines custom datatypes to retrieve data
from the UAV, which are enclosed in a verbatim HASKELL block. The output stream
all_ok_capturing assesses that, whenever the vehicle is taking a picture, the height,
roll and pitch are acceptable and the vehicle is near the target location. The output
stream flying_in_safe_zones reports if the UAV is flying outside every forbid-
den region. The output stream depth_into_poly takes the minimum of the distances
between the vehicle position and every side of the forbidden region inside which the
vehicle is.
     Available at
354     F. Gorostiaga and C. Sánchez

format JSON
use theory Geometry2D
use library Utils
use haskell Data.Maybe
use haskell Data.List
data Attitude = Attitude {yaw :: Double, roll :: Double, pitch :: Double}
                         deriving (Show,Generic,Read,FromJSON,ToJSON)
data Target   = Target {x :: Double, y :: Double, num_wp :: Double} ...
data Position = Position {x :: Double, y :: Double, alt :: Double} ...
input     Attitude attitude
input      Vector2 velocity
input     Position position
input       Double altitude
input       Target target
input [[[Double]]] nofly
input     [String] events_within

output Bool all_ok_capturing = capturing [now] ‘implies‘
  (height_ok [now] && near [now] && roll_ok [now] && pitch_ok [now])
output Bool flying_in_safe_zones = ’isNothing (flying_in_poly [now])
output (Maybe Double) depth_into_poly = let
  mSides = ’(fmap polygonSides) (flying_in_poly [now])
  distance_from_pos = ’shortestDist (filtered_pos [now])
  in    2’fmap distance_from_pos mSides
  where shortestDist x = (distancePointSegment x)
define Bool capturing = ...
define Double filtered_pos_component <(Position->Double) field> <String nm> = ...
define Double filtered_pos_x      = filtered_pos_component x "x" [now]
define Double filtered_pos_y      = filtered_pos_component y "y" [now]
define Double filtered_pos_alt = filtered_pos_component alt "alt" [now]
define Point2 filtered_pos = ’P (filtered_pos_x [now]) (filtered_pos_y [now])
define Bool near = let target_pos = ’targetToPoint (target [now])
  in    2’distance (filtered_pos [now]) target_pos < 1
  where targetToPoint (Target x y _) = P x y
define Bool height_ok = filtered_pos_alt [now] > 0
define Bool roll_ok     = ’(abs.roll) (attitude [now]) < 0.0523
define Bool pitch_ok    = ’(abs.pitch) (attitude [now]) < 0.0523
define [Polygon] no_fly_polys = ...
define (Maybe Polygon) flying_in_poly = let
  position_in_poly = ’pointInPoly (filtered_pos [now])
  in 2’find position_in_poly (no_fly_polys [now])

Intermediate stream capturing captures whether the UAV is taking a picture (omitted
for brevity). The streams filtered_pos_alt and filtered_pos represent the loca-
tion and altitude of the UAV filtered to reduce noise from the sensors. We omit the defi-
nition of the filter, which is implemented in filtered_pos_component The streams
height_ok, roll_ok, and pitch_ok, calculate that the corresponding attitude of the
vehicle is within certain boundaries. Finally, the intermediate stream no_fly_polys
obtains a set of Polygons from the input forbidden regions (its definition has been omit-
ted), and the stream flying_in_poly returns the forbidden region in which the vehi-
cle is flying, if any. The artifact attached to this paper includes more UAV specifications,
which have been validated in real missions [25].
            HLola: a Very Functional Tool for Extensible Stream Runtime Verification          355

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