Reconstructing Software Architecture

Oct 6, 2013Download as pptx, pdf6 likes8,147 views

Software architecture reconstruction is the process of analyzing an existing software system to produce an architectural representation of it. It involves extracting information from various sources like code, documentation, and profiles. This information is converted into a standard database format and combined from different sources through view fusion. The main activities of reconstruction are visualization and interaction with the views, and recognizing patterns in the code. It is an iterative process requiring both technical expertise and architectural skills to understand dependencies and evaluate how an implemented system conforms to its intended architecture.

Reconstructing software
architecture
DR. HIMANSHU HORA
SRMS College of Engg. & Tech., Bareilly
INDIA

Introduction:
 Architecture Reconstruction : is an “as-built”
architecture of an implemented of system i.e
obtained from the existing system.
 It is done through the “detail analysis” of the system,
using tool support.
 If the tools are successful, the end result is an
architectural representation.

What is Architecture Reconstruction?
 Although it is not an automatic process
 It is an “interpretive” , “interactive”, and “iterative”
process involving many activities.
 Requires the skills and attention of both the
engineering expert and the architect

Uses:
 To “redocument” the project architecture
 To understand the architectural dependencies in
embedded control software
 To understand different systems in the automotive
industry
 To evaluate the conformance of satellite ground system’s
implementation to its reference architecture

Reconstruction Activities:
Software architecture reconstruction comprise the
following activities,carreid out iteratively:-
 Information Extraction;
 Database Construction;
 View Fusion;
 Reconstruction.

Figure 1: Architecture Reconstruction activities

Information Extraction:
 The purpose of this activity is to extract information
from vavious source
 To extract information, a variety of tools are used:
* Parser(ex Imagix,CIA etc)
* Abstract syntax tree analyser (ex,Refine etc)
* Lexical analyser (ex,LSME)
* Profilers(ex,gprof)
*Ad hoc (ex,grep,perl etc)

Guidelines(Information Extraction):
 Use the “least effort” extraction
 Validate the information you have extracted
 Extract dynamic information where required

Database construction:
 Database construction involves converting
information into a standard form, such as Rigi
Standard Form(tuple based data format) & SQL
based database format
 It is necessary to chose a database model

For choosing model,consider the following:
 It should be a well known model
 Sufficient queries are allowed
 Support remote access to database
 Suppoerts view fusion by combining information
from various tables
 It supports query language

Guideline(database construction):
 Build database table from the extracted relations to
make processing of database view easier
 Carefully consider the database design
 Use simple lexical toollike perl and AWK

View Fusion:
 It combines information in the database to produce a
coherent view of architecture
 It involves defining and manipulating extracted
information to reconcile, augment ,and establish
connections between the elements

Guideline(View Fusion):
 Fuse table when no single extracted table provides
the needed information
 Fuse table when there is an ambiguity
 Consider different extraction techniques to extract
different information

Reconstruction:
 The reconstruction activity is where the main work of
building abstraction and various representation of the
data to generate an architecture representation take
place
 Reconstruction provides two basic activities:
* visualisation and interaction &
* pattern recognition and definition

 visualisation and interaction: A mechanism by
which the user may interactively visualise, explore
and manipulate views
pattern recognition and definition:provides
facilities for architectural reconstruction: the
definition and recognition of the code menifestation
of architectural patterns

Thank You
DR. HIMANSHU HORA
SRMS College of Engg. & Tech., Bareilly
INDIA

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Creational patterns deal with object creation and aim to create objects in a suitable manner. There are three main creational patterns: the factory pattern, which provides a consistent interface for creating properly configured objects; the abstract factory pattern, which is a factory for factories and handles more complex situations; and the singleton pattern, which ensures a single instance of an object and consistency of data by restricting object instantiation.

Architecture ReviewHimanshu

The document discusses architecture reviews, which assess an architecture's ability to fulfill quality requirements and identify risks. It describes the goals of ensuring documentation, coherence, standards compliance, and achieving project goals. Types of reviews include project process, purchase process, and iterative reviews. The basic review flow involves submitting documents, review, resolving issues, and approval or rejection. Roles in the process include the moderator, recorder, reviewers, and author/design team. Benefits are identifying risks, assessing quality attributes, promoting practices, and capturing design rationale.

Reliability and its principalsHimanshu

The document discusses the principles of reliability in software engineering. It defines reliability as the probability of failure-free operation over a specified time period and environment. There are four key factors that influence reliability: numerical value, intended function, life, and environmental conditions. The most important aspects that impact reliability are design, production, and transportation. Reliability can be achieved through simple design, redundancy, overdesign, fail-safe devices, and proper maintenance. Related concepts discussed include availability, maintainability, and principles of software engineering and validation.

Structural and functional testingHimanshu

This document discusses structural and functional testing. Structural testing generates test cases based on the internal structure of a program, while functional testing generates test cases based on the program's functionality without considering internal structure. Some types of structural testing include statement coverage, branch coverage, path coverage, and conditional coverage. Types of functional testing include equivalence class partitioning and boundary value analysis. Testing has limitations in that it can find errors but not prove their absence, does not help find root causes, and only identifies known issues without uncovered defects.

White box black box & gray box testingHimanshu

This document discusses and compares white box, black box, and gray box testing methods. White box testing examines internal program structure and logic and requires knowledge of source code. Black box testing examines functionality without knowledge of internal workings. Gray box testing combines aspects of white and black box by having limited internal knowledge. Each method has advantages like code coverage for white box or large tester pools for black box, and disadvantages like cost or limited coverage. The document provides details on each method and how they differ in tester knowledge and perspective.

Pareto analysisHimanshu

Pareto analysis is a technique used to identify the key causes that contribute to a problem. It is based on the principle that 80% of problems are caused by 20% of the causes. The document outlines the history and procedure of Pareto analysis. The procedure involves gathering data on causes of a problem, ranking them by frequency, plotting them on graphs, and using the graphs to identify the vital few causes that contribute to 80% of the problem. An example is provided where Pareto analysis is used to identify the main causes of long waiting times at a clinic. The analysis finds that 7 main causes account for 80% of the waiting time problem.

Load runner & win runnerHimanshu

This document provides an introduction and overview of Load Runner and Win Runner software. Load Runner is an automated performance testing tool from HP that examines system behavior under load. Win Runner is an automated functional GUI testing tool that allows recording and playback of user interactions as test scripts. Both were originally developed by Mercury Interactive, which was acquired by HP. The document describes how each tool works, their typical processes, and provides some facts about Load Runner and Win Runner.

Crud and jadHimanshu

The document discusses CRUD (Create, Read, Update, Delete) operations and JAD (Joint Application Development). CRUD represents the basic SQL operations - Create, Read, Update, Delete. A CRUD analysis validates that a data model accounts for all required create, retrieve, update, delete functions. JAD is a process where developers, managers, users work together to build a product using structured interview sessions over 3-6 months. It aims to improve quality, communication and reduce costs and errors compared to traditional development.

Junit and cactusHimanshu

The document discusses the automated testing tools JUnit and Cactus. It outlines some pitfalls of using these tools, such as not using assertions correctly, writing unfocused test methods, and failing to isolate tests. However, it does not mention important drawbacks like the high cost and complexity of distributed testing with Cactus. Alternative tools like HttpUnit may provide simpler web testing without requiring test code on multiple JVMs. While automated testing can save time and money, tools like JUnit and Cactus have limitations and are not perfect for all types of testing.

Risk based testing and random testingHimanshu

The document discusses risk based testing and random testing approaches. It outlines the challenges of time and resource constraints when testing software. Risk based testing uses risk analysis and metrics to focus testing on high risk areas in order to save time and money while maintaining quality. Metrics are developed to manage and organize large test projects. Random testing involves automatically generating random inputs and scenarios to stress test software in ways not covered by nominal testing. It can be used with differential and fault injection testing against a reference implementation to automatically check test results.

Testing a data warehousesHimanshu

The document discusses testing of data warehouses. It notes that organizations need data warehouse testing more than ever to check data quality, completeness, and correct transformations. The document outlines different types of data warehouse testing and the testing process. It emphasizes planning testing phases and staffing as well as avoiding risks. Key focus points in testing include underlying data and data warehouse components. The document also compares database testing to data warehouse testing and notes challenges of testing including data selection from multiple sources and inconsistent data.

Software testing tools and its taxonomyHimanshu

This document discusses software testing tools and proposes a taxonomy for classifying them. It begins by addressing common myths and facts about software testing and developers. It then provides definitions of software testing and examples of over 20 specific software testing tools. The document proposes that a taxonomy is needed to classify tools to help testers choose the right ones. It reviews existing tool taxonomies and their shortcomings before concluding and thanking the reader.

Software reliability engineering processHimanshu

The document outlines the software reliability engineering (SRE) process. It defines software reliability as the ability of a system to perform without failure under specified conditions for a given time period. The SRE process involves 6 steps: 1) listing systems to test, 2) developing operational profiles, 3) defining acceptable reliability levels, 4) preparing tests, 5) executing tests, and 6) guiding the testing and release process. The goal is to measure, predict, and manage reliability through statistical modeling from the customer's perspective of minimizing failures.

Software reliability growth modelHimanshu

This document discusses software reliability growth models, which use system test data to predict the number of defects remaining in software and determine if the software is ready to ship. Most models have a parameter related to the total number of defects. Knowing the number of residual defects helps decide how much more testing is needed. Examples of models include the Goel-Okumoto model, which models the failure rate as approaching a total number of defects over time. The assumptions of the Goel-Okumoto model include that failure times are exponentially distributed and the number of failures follows a non-homogeneous Poisson process.

Software reliability tools and common software errorsHimanshu

The document discusses various software reliability tools and common software errors. It describes tools like SMERFS, SRMP, SoftRel, and CASRE which are used for statistical modeling and estimation of software reliability. SMERFS is designed to estimate and predict reliability during testing using failure data. SRMP contains nine models and uses maximum likelihood estimation. SoftRel simulates project occurrences using a Piecewise-Poisson Markov Process. CASRE was developed by NASA to make reliability estimation easier. Common software errors discussed include errors in handling, preventing, detecting and recovering from errors, as well as boundary, calculation, initial state, race condition and load condition errors.

Structural patternsHimanshu

Software product lineHimanshu

Shared information systemsHimanshu

SaamHimanshu

Design PatternHimanshu

Creational patternHimanshu

Architecture ReviewHimanshu

Reliability and its principalsHimanshu

Structural and functional testingHimanshu

White box black box & gray box testingHimanshu

Pareto analysisHimanshu

Load runner & win runnerHimanshu

Crud and jadHimanshu

Junit and cactusHimanshu

Risk based testing and random testingHimanshu

Testing a data warehousesHimanshu

Software testing tools and its taxonomyHimanshu

Software reliability engineering processHimanshu

Software reliability growth modelHimanshu

Software reliability tools and common software errorsHimanshu

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