Information and Knowledge Base Systems To Support Software Architecture Extraction and Search for Re

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Information and Knowledge Base Systems To
Support Software Architecture Extraction and
Search for Reusable Software Components

• CPRE 416-Software Evolution and
  Maintenance-Lectures 11-13

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Contents

• Introduction a broad picture of the software
  engineering problems we are trying solve.
• Theme we need a language to think about
  software is structured, how it operates ..
• Discussion of one paper LaSSIE system.
• What language does LaSSIE use? Why?

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Introduction No Silver Bullet

• Four essential difficulties in building large
  software
• Complexity The complexity of development
  process, application domain etc.
• Conformity Software cannot have a regular formal
  structure because it must adapt to and interact
  with human and institution needs
• Changeability software changes often
• Invisibility The structure of software is hidden
• Brooks, F. P., No Silver Bullet Essence and
  Accidents of Software Engineering, IEEE Computer
  Magazine, April, 1987.

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LaSSIE a Knowledge-based Software Information
System

• Address the invisibility problem directly and
  the complexity is addressed indirectly.
• Invisibility implies it is hard to look for
  things of concern another way to say that
  searching is hard.
• Their view of challenges (a) semantic
  proliferation, (b) multiple views, (c)
  intelligent indexing.
• Their solution Knowledge representation and
  reasoning technology.

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Discovery Task

• Discovery Acquiring the knowledge of application
  domain, architecture and design, specific
  practices.
• Discovery takes 30 to 60 of developers time.

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Discovery Series of Questions

• Typical questions about the ATT Telephone
  Switching System

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Answers require different views

• Answers depend on knowledge associated with views
  of the system

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A view of invisibility
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Retrieve reusable components

• My interpretation of this line of research
• Software is considered as a collection of
  artifacts.
• A mechanism to retrieve artifacts.
• Retrieval Mechanisms (RMs) keyword-based,
  faceted-index-based, or semantic-net based ..
• The retrieval mechanism uses a language to
  refer to software artifacts and the relationships
  between those artifacts.

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Problems with Keywords

• Semantics of the keywords not available to the
  retrieval or the storage mechanism.
• Different keywords for the same thing.
• Meaning of the keyword changes depending on the
  context.

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Faceted Classification

• Examples of facets functions, objects etc.
• Values for the function could be add, subtract,
  append, open, read, write ..
• Values for the object could be array, file ..
• Retrieval by specifying facets and their values.
• Classification scheme is static.
• Weak representation cannot express constraints.

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Why are the RMs inadequate

• According to LaSSIE creators
• The RMs are like librarians
• These librarians do not think of the software
  artifacts as parts of an architecture.
• They organize and retrieve the software artifacts
  without using any knowledge of their
  architectural organization.
• The retrieval does not have an intelligent
  indexing scheme that incorporates knowledge about
  the architecture.

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Another view of inadequacies

• The language used by RMs is inadequate
• The description of each software artifact should
  include constraints on how it should be reused.
• The RMs have a language to specify mere
  associations not a language to specify
  constraints.

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An Observation

• An observation from the LaSSIE paper about RMs

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What LaSSIE attempts

• An information retrieval system with a knowledge
  base (KB) to integrate architectural, conceptual,
  and code views.
• Uses the knowledge representation language called
  KANDOR.
• Provides a UI with graphical browser and natural
  language query processing.

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What goes in the KB

• Actions and Objects
• Choice based on
• Existing work on domain analysis
• Empirical studies of programming knowledge.
• Vocabulary used by programmers at ATT
• Domain analysis includes the description of a set
  of components - actions and objects.
• Programming Knowledge
• Syntactic structural details of the programming
  language
• Semantic the action-object paradigm prior work
  by another researcher.

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Programmers Vocabulary

• Samples comments from ATT programmers.

• Describe simple actions sometimes caused by other
  actions include operands, actors, and contexts
  (customer feature in this case).

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Knowledge Representation Language

• Frame-based representation is chosen for LaSSIE.
• Frames represent classes of objects.
• KANDOR language chosen.

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Frame-based Languages

• A frame represents a collection of instances
  (related to classes, concepts).
• Each frame has a set of slots (also called
  attributes) which can be filled by values or
  other frames.
• Frames can have a kind-of slot (is-a link) to
  enable taxonomy.
• A script is a type of frame that describes what
  happens temporally the usual example given is
  that of describing going to a restaurant. The
  steps include waiting to be seated, receiving a
  menu, ordering, etc.
• Ronald J. Brachman wrote a paper titled "What
  IS-A is and isn't", wherein 29 different
  semantics were found in projects.

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A Frame Example
frame-27 name ("Adam") sex (frame-2)
spouse (frame-28) child (frame-29 frame-30
frame-31)
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More About Frames

• If a frame f is in a relationship r to a frame g,
  then we put the value g in the r slot of f.
• A frame can be considered just a convenient way
  to represent a set of predicates applied to
  constant symbols (e.g. ground instances of
  predicates.). For example, the previous frame
  could be represented by
• sex(Adam,Male)
• spouse(Adam,Beth)
• child(Adam,Charles) etc.

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Access Paths

• It is a search mechanism.
• Example
• ( ( parent John ?X) (sister ?X ?Y) )

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Rules

• Logical implications are represented as rules.
• Example
• ((spouse ?X ?Y) -gt ((spouse ?Y ?X)
• It is a forward chaining rule. It says whenever
  we learn a relation (spouse f g) we should
  immediately conclude (spouse g f)

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Queries and Assertions

• Two basic operations on a knowledge-base --
  queries  and assertions.
• Queries retrieve knowledge from the
  knowledge-base, applying if-needed rules.
• Assertions add facts to the knowledge-base and
  apply if-added rules.
• An example of if-needed rule
• (( aunt ?Z ?Y) ?(parent ?Z, ?X) (sister ?X, ?Y)