Advanced Topics in Software Engineering

Cmpe 550

Fall 2008

Is There a Problem?

• 7 out of every 10 major high risk development programs are encountering software problems and rate is increasing.

• The development of large applications in excess of 5000 function points (~500,000 LOC) is one of the most risky business undertakings in the modern world

Is There a Problem?

• The risks of cancellation or major delays rise rapidly as the overall application size increases– 65% of large systems (over 1,000,000 LOC)

are cancelled before completion– 50% for systems exceeding half million LOC– 25 % for those over 100,000 LOC

• Failure or cancellation rate of large software systems is over 20%

Is There a Problem?

• After surveying 8,000 IT projects, Standish Group reported about 30% of all projects were cancelled.

• Average cancelled project in U.S. is about a year behind schedule and has consumed 200% of expected budget

• Work on cancelled projects comprises about 15% of total U.S. software efforts, amounting to as much as $14 billion in 1993 dollars.

Is There a Problem?

• Of completed projects, 2/3 experience schedule delays and cost overruns

• 2/3 of completed projects experience low reliability and quality problems in first year of deployment

• Software errors in fielded systems typically range from 0.5 to 3.0 occurrences per 1000 lines of code

• Have you ever been on a project where the software was never finished or used?

• What were some of the problems?

Types of Problem Projects

• Mission Impossible– Likely to succeed, happy workers

• Ugly– Likely to succeed, unhappy workers

• Kamikaze– Unlikely to succeed, happy workers

• Suicide– Unlikely to succeed, unhappy workers

Understanding the Problem

Understanding the Problem

Software Evolution (Maintenance)

• Belady and Lehman’s Laws:– Software will continually change.– Software will become increasingly

unstructured as it is changed.

• Leveson’s Law:– Introducing computers will not reduce

personnel numbers or costs.

• Is software engineering more difficult than hardware engineering?

• Why or why not?

Why is software engineering hard?

• "Curse of flexibility"

• Organized complexity

• Intangibility

• Lack of historical usage information

The Curse of Flexibility

• "Software is the resting place of afterthoughts."• No physical constraints

– To enforce discipline on design, construction and modification– To control complexity

• So flexible that start working with it before fully understanding what need to do

• The untrained can get partial success.– "Scaling up is hard to do"

• ‘‘And they looked upon the software and saw that it was good. But they just had to add one other feature ...’’

Complexity

• The underlying factor is intellectual manageability• 1. A "simple" system has a small number of unknowns in

its interactions within the system and with its environment.

• 2. A system becomes intellectually unmanageable when the level of interactions reaches the point where they cannot be thoroughly– planned– understood– anticipated– guarded against

Dealing with Complexity

• Analytic Reduction (Descartes)– Divide system into distinct parts for analysis

purposes.– Examine the parts separately.

• Three important assumptions:– The division into parts will not distort the phenomenon

being studied.– Components are the same when examined singly as

when playing their part in the whole.– Principles governing the assembling of the

components into the whole are themselves straightforward.

Dealing with Complexity

• Statistics– Treat as a structureless mass with

interchangeable parts.– Use Law of Large Numbers to describe

behavior in terms of averages.

• Assumes components sufficiently regular and random in their behavior that they can be studied statistically.

Software

• Too complex for complete analysis:– Separation into non-interacting subsystems

distorts the results.– The most important properties are emergent.

• Too organized for statistics– Too much underlying structure that distorts

the statistics.

AREAS FOR FUTURE RESEARCH

why software is important?

SW Research FP6

•Fixed communications•Mobile communications•PDAs•Home Platforms

New Services

SOFTWARELifeblood of the Information Society

• The crucial component in this interconnected world will be the software

• Software technologies are already crucially important for the development of advanced products and services

SOFTWAREThe Market

• The European Software Market – Forms about a third of the global software

market– Has expected annual growth of 3-4% in the

coming 5 years– 2005: 74 billion Euros

• Software accounts for the 10% of the total ICT market in Europe

Research Challenges

• Trend to large, heterogenous, distributed sw systems leads to an increase in system complexity

• Software and service productivity lags behind requirements

• Increased complexity takes sw developers further from stakeholders

• Importance of interoperability, standardisation and reuse of software increasing.

Research Challenges

• Service Engineering

• Complex Software Systems

• Open Source Software

• Software Engineering Research

Software Engineering Research Approaches

• Balancing theory and praxis

• How engineering research differs from scientific research

• The role of empirical studies

• Models for SE research

The need to link research with practice

• Why after 25 years of SE has SE research failed to influence industrial practice and the quality of resulting software?

• Potts argues that this failure is caused by treating research and its application by industry as separate, sequential activities.

• What he calls the research-then-transfer approach. The solution he proposes is the industry-as-laboratory approach.

.

Colin Potts, Software Engineering Research Revisited, IEEE Software, September 1993

Research-then-Transfer

Incremental Refinementof research solutions

Problem evolves invisibly to the research community

ProblemV1

Wide gulf bridgedby indirect, anecdotal knowledge

ResearchSolutionV1

Technologytransfer Gapbridged byhard, but frequentlyinappropriate technology

ProblemV2

ProblemV3

ProblemV4

ResearchSolutionV2

ResearchSolutionV3Research

SolutionV4

Research-then-Transfer Problems

• Both research and practice evolve separately• Match between current problems in industry

and research solutions is haphazard• No winners

Disadvantages of Research-then-Transfer

• Research problems described and understood in terms of solution technology - whatever is current research fashion. Connection to practice is tenuous.

• Concentration is on technical refinement of research solution - OK but lacks industrial need as focus, so effort may be misplaced.

• Evaluation is difficult as research solutions may use technology that is not commonly used in industry

• Delay in evaluation means problem researchers are solving has often evolved through changes in business practice, technology etc.

• Transfer is difficult because industry has little basis for confidence in proposed research solution.

Industry-as-Laboratory Approach to SE research

ProblemV2

Problem

V1

Problem

V3

Problem

V4

ResearchSolution

V1

ResearchSolution

V2

ResearchSolution

V3

ResearchSolution

V4

Advantages of Industry-as-Laboratory Approach

• Stronger connection at start because knowledge of problem is acquired from the real practitioners in industry, often industrial partners in a research consortium.

• Connection is strengthened by practitioners and researchers constantly interacting to develop the solution

• Early evaluation and usage by industry lessens the Technology Transfer Gap.

• Reliance on Empirical Research– shift from solution-driven SE to problem-focused SE

– solve problems that really do matter to practitioners

Early SEI industrial survey research

• What a SEI survey* learned from industry:– There was a thin spread of domain knowledge in most

projects– Customer requirements were extremely volatile.

• These findings point towards research combining work on requirements engineering with reuse - instead of the approach of researching these topics by separate SE research communities - as is still found today!

*From ‘A field study of the Software Development Process

for Large Systems’, CACM, November 1988.

Further Results from Potts et al Early 90s Survey

23 software development organizations (during 1990-92). (Survey focused on Requirements Modeling process)

• Requirements were invented not elicited.• Most development is maintenance.• Most specification is incremental.• Domain knowledge is important.• There is a gulf between the developer and user• User-interface requirements continually change.• There is a preference for office-automation tools over

CASE tools to support development. I.e. developers found using a WP + DB more useful than any CASE tools.

Industry-as-Laboratory emphasizes Real Case Studies

Advantages of case studies over studying problems in research lab.

• Scale and complexity - small, simple (even simplistic) cases avoided - these often bear little relation to real problems.

• Unpredictability - assumptions thrown out as researchers learn more about real problems

• Dynamism - a ‘real’ case study is more vital than a textbook account

The real-world complications of industrial case studies are more likely to throw up representative problems and phenomena than research laboratory examples influenced by the researchers’ preconceptions.

Need to consider Human/Social Context in SE research

• Not all solutions in software engineering are solely technical.

• There is a need to examine organizational, social and cognitive factors systematically as well.

• Many problems are “people problems”, and require “people-orientated” solutions.

Theoretical SE research

• While there is still a place for innovative, purely speculative research in Software Engineering, research which studies real problems in partnership with industry needs to be given a higher profile.

• These various forms of research ideally complement one another.

• Neither is particularly successful if it ignores the other.• Too industrially focused research may lack adequate

theory!

• Academically focused research may miss the practice!

Research models for SE

• Problem highlighted by Glass*: Most SE Research in 1990s was Advocacy Research.

Better research models needed.

• The software crisis provided the platform on which most 90s research was founded.

• SE Research ignored practice, for the most part; lack of practical application and evaluation were gapping holes in most SE research.

• Appropriate research models for SE are needed.* Robert Glass, The Software -Research Crisis, IEEE Software, November 1994

Methods underlying Models

• Scientific method

• Engineering method

• Empirical method

• Analytical methodFrom W.R.Adrion, Research Methodology in Software

Engineering, ACM SE Notes, Jan. 1993

Scientific method

Observe real world

Propose a model or theoryof some real world phenomena

Measure and analyzeabove

Validate hypotheses of the model or theory

If possible, repeat

Engineering method

Observe existing solutions

Propose better solutions

Build or develop bettersolution

Measure, analyze, andevaluate

Repeat until no further improvements are possible

Empirical method

Propose a model

Develop statistical or otherbasis for the model

Apply to case studies

Measure and analyze

Validate and then repeat

Analytical method

Propose a formal theory or set ofaxioms

Develop a theory

Derive results

If possible, compare withempirical observations

Refine theory if necessary

Need to move away from purely analytical method

• The analytical method was the most widely used in mid-90s SE research, but the others need to be considered and may be more appropriate in some SE research.

• Good research practice combines elements on all these approaches.

4 important phases for any SE research project (Glass)

• Informational phase - Gather or aggregate information via

– reflection

– literature survey

– people/organization survey– case studies

• Propositional phase - Propose and build hypothesis, method or algorithm, model, theory or solution

• Analytical phase - Analyze and explore proposal leading to demonstration and/or formulation of principle or theory

• Evaluation phase - Evaluate proposal or analytic findings by means of experimentation (controlled) or observation (uncontrolled, such as case study or protocol analysis) leading to a substantiated model, principle, or theory.

Software Engineering Research Approaches

• The Industry-as-Laboratory approach links theory and praxis

• Engineering research aims to improve existing processes and/or products

• Empirical studies are needed to validate Software Engineering research

• Models for SE research need to shift from the analytic to empirical.