Like all Non-functional Requirements (NFRs), Information Quality (IQ) requirements are used to be represented as softgoals that are difficult to be represented measurably. However, several recent studies argued that many requirements that are classified as NFRs can be expressed in a measurable way. This paper reports on experience gained while proposing a goal-based approach for capturing IQ requirements as softgoals at a high-level of abstraction and then refining them until reaching their operational specifications.
1 For more information about the case study please refer to [
The rest of this report is structured as follows; Section 2 presents the approach, and its implementation and evaluation are discussed in Section 3. Section 4 discusses the findings, and the report is concluded in Section 5. 2
The process that underlies the goal-based approach for specifying IQ requirements is depicted in Figure 1, and it consists of two main phases: 1. Modeling phase aims at modeling the IQ requirements in their social and organizational context. This phase consist of eight main steps: (1.1) Actor modeling, models the main actors of the system in terms of agents and the roles they are playing; (1.2) Goal modeling, models the actors’ objectives in terms of goals, and refine them until reaching their leaf goals; (1.3) Information modeling, models the different relations between goals and information; (1.4) Social dependency modeling, models actor dependencies for information, and the delegations of permissions and goals; (1.5) Trust modeling, models trust/distrust relations among actors concerning their social dependencies.
The following three steps (S.1, S.2 and S.3) are specialized for dealing with
IQ softgoals, and they can start after (1.3) Information modeling step:
(S.1) IQ softgoal modeling aims at modeling top-level IQ softgoals and refines
them into a suitable granularity that enables for their approximation. Usually,
softgoals refinement into more specific sub softgoals can be done based on
taxonomy (e.g., [
Following [
1.1 Actor modeling 1.2 Goal modeling
Define refinement taxonomy
Define classification criteria Define quality spaces 2.
the application of the taxonomy for refining a top-level IQ softgoal concerning information (e.g., “Trading order”). After refining all top-level IQ softgoals into their leaf IQ softgoal based on the taxonomy, we can proceed to the next step.
(S.2) Leaf IQ softgoal classification aims at identifying classification criteria
to be followed while organizing leaf IQ softgoals into groups concerning their
satisfaction criteria, since they capture different IQ dimensions (e.g., accuracy,
completeness) and they might need to be approximated in different ways.
Define classification criteria. Following Glinz [
(S.3) Leaf IQ softgoal approximation aims at approximating leaf IQ softgoals
into IQ Constraints (IQCs). In particular, a softgoal can be satisfied by a quality
constraint through the approximation relation [
Value completeness
IQ
Accuracy
of provenance
Accessibility Believability Purpose of use Trustworthiness Trustworthiness completeness of the source of the provision
TO is accessible
High quality Trading Order (TO)
and TO is valid
TO is consistent
TO is accurate
and TO is TO is trustworthy TO is complete concerning its provenance believable
and and
TO is complete TO is value TO source is TO providence
for purpose of use complete trustworthy is trustworthy
vides clear criteria for the satisfaction of a softgoal. However, an approximation
relation can hold only if a well-defined quality space exists [
Define quality spaces. IQ dimensions must be associated with specific measures (quality space) to assure their effective assessment. To this end, a quality space for each IQ dimension has been defined based on the following criteria: the measurement should be easy to be interpreted, the difference between the value levels of measurement must be meaningful, and the calculation of such values should be consistent. For instance, timeliness and consistency are time-related aspects; therefore, they are measured and calculated in seconds. How IQ dimensions are analyzed should be clearly defined as well, e.g., validity is analyzed by comparing the currency (seconds) of information with its volatility (seconds), and if its currency is smaller than its volatility it is valid, otherwise, it is invalid.
After defining measures for analyzing leaf IQ softgoals, we can approximate them into IQCs. Three types of IQCs have been defined (shown in Table 1): (1) Operational IQC define actions to be performed in an already determined context. E.g., believability softgoal is approximated into operational IQC that define min/max values concerning information believability. (2) Declarative IQC define properties of the system that should hold. E.g., trustworthiness of provision softgoal is approximated into declarative IQC stating that information should be transferred only through IP provision. (3) Quantitative IQC specify properties of the system that should hold, and can be measured on an ordinal scale. E.g., consistency softgoal is approximated into quantitative IQC stating that information should have the same currency among its interdependent readers that are actors who use the same information for interdependent purposes.
Figure 4 shows a portion of the stock market system model represented with
an extended modeling language [
The language relies on four types of permissions for capturing accessibility. Moreover, read and produce relationships have been extended with believability checks to capture believability. Trustworthiness is analyzed based on the trustSG1.1 High quality [Trading order]
and SG1.5.1 [Trading order] is believable x p a The value of a trading order should fit in [min/max values] Role play Agent O info
agent instantiation own D depending
on a trader T In[Rv]e[Mst]o[Sr]’s orders P [ipPn]ef[roRmr]mi[sMastii]oo[nSn] coqnauspatlxriatiynt DTT/ Gopaelr/minfo DTT/ R delegation approximation trust/distrust produce read send modify St1ra:dMinagkesepcruorfiittiebsy tSrmadaelrl
and S1.2.1: Manage S1.2.2: decide Investors’ orders trading options
S R M P S
Investors’ Trading orders orders
J1: Make profit from trading securities and
J1.2: trade depending on a trader J1.1: trade by itself
S P Investors’
orders Investors’ orders [IP] [Time]
T
Jack
O softgoal information DdelGegoaatlioDn [Pin/IpfPro]orvm[Tisaiitmoioenn]
Investors’ orders [IP] [Time] worthiness of both the source and the provision. Accuracy is captured relying on both believability and trustworthiness. Completeness is analyzed based on the provision type and the part of concept that captures the relationship between information and its sub-parts. The language proposes information volatility, read and send timeliness concepts for capturing timeliness. Finally, it provides interdependent readers and read time concepts for analyzing consistency.
2. Analysis phase. To verify the IQ requirements model, all the proposed concepts have been formalized, and reasoning axioms have been developed relying on Disjunctive Datalog. Moreover, a set of Properties of the Design (PoD) have been defined. The model is considered correct and consistent if all of the PoD hold. If any of them is violated (e.g., information is inaccurate, incomplete, inconsistent, etc.), the designer is notified about that, which allows her to modify the model to address such violation. 3
The approach has been evaluated depending on a simulation method, developing a prototype implementation and tests its applicability by applying it to the Flash Crash case study. The approach was able to models and effectively analyze (e.g., detecting any violation to the properties of the design) the IQ requirements of the case study. Moreover, the scalability of the reasoning support of the approach has been evaluated, and it was able to deal with sufficiently large models2. 4
Most software engineers need to deal with NFRs, yet some of these NFRs can be
responsible for providing essential functionality for the system such as security,
2 For more information about the implementation and evaluation please refer to [
This paper reports on experience gained while proposing a goal-based approach for capturing IQ requirements as softgoals at a high-level of abstraction and then refining them until reaching their operational specifications. The focus was put on making the approach easy to be understood and used, and each of its steps was accompanied by a detailed description of how it can be performed. This may help other scholars while dealing with NFRs.