Cast WP Lean Application Management Web

8/3/2019 Cast WP Lean Application Management Web

1/18

White Paper

Cutting IT Costs by Applying

Lean Principles

Software Measurement

for Lean Application Management

IT organizations are experimenting with Lean techniques to

reduce application costs and delivery times with mixed results.

To achieve these objectives, IT must eliminate the largest source

of waste in application development and maintenance defects

and the enormous rework they cause. This paper shows how

focusing on the Jidoka pillar of the Toyota Production System,

which is the use of automation to detect and eliminate defects

early, attacks and reduces defects and rework. By applying

the waste-reducing principles of Jidoka to development,

maintenance, and the design of applications, IT can both cutcosts and shorten time-to-service.

Dr. Bill CurtisSenior Vice President and Chief Scientist, CAST

Director, Consortium for IT Software Quality


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Cutting IT Costs by Applying Lean Principles

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Executive Summary

Dr. Bill Curtis, SVP and Chie Scientist at CAST, explains how the Lean

practices pioneered in the Toyota Production System apply to the development

and maintenance o application sotware, leading to decreased total cost o

ownership and improved business responsiveness and operational dependability.

The Problem

ITorganizationshavebeenapplyingLeanpracticestothedevelopmentand

maintenanceofapplicationsoftware,yetthepowerfulbenetshaveyetto

befullyrealizedbecausetheyhavenotfocusedontheearlydetectionand

eliminationofdefects.

Defectsinsourcecodeandthereworktheycausearethelargestsourcesof

wasteinapplicationdevelopmentaccountingfor30%to50%ofdevelopment

effort.

Asmuchastwothirdsofallapplicationmaintenanceeffortcanbeclassiedaswasteduetoreworkcausedbydefectsandtimespenttryingtounderstand

whatisgoingoninpoorlyconstructedapplications.

Overtime,applicationsbecomelessresponsiveandobesewithcodethatisno

longerneeded,wastingmachineresources.

ThefastpaceandshortreleasecyclesofAgiletruncatesthetimeavailable

foridentifyingandremediatingstructuralproblemsinthesourcecodeofan

application.

The Solution

ByfocusingonJidoka,thepillarofLeaninvolvingtheautomatedearly

detectionandeliminationofdefects,defectscanbeidentiedwhentheyare

injectedintothesourcecodeandxedquickly,ratherthanlaterinthelife

cyclewhentheirrepairisoften10timesmoretime-consumingandexpensive.

Executive Summary


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Eliminatingthecausesofdefectsandcontinuallytrainingdevelopersfordefect-

freeworkcandramaticallyimproveproductivityandshortentime-to-release.

Inapplicationmaintenance,itisvitaltoaddressthechangeabilityofcode

beforeitsreleasetoreducethetimeneededtounderstandtheapplicationfor

subsequentreleases,andsustainthequalityoftheapplicationoveritslifetime.

Overanapplicationsoperationallife,youmustcontinuouslytunetheperformanceofthecodeandmanageapplicationsize,especiallywhen

deployingitinacloudenvironment.

Key Takeaways

ThroughadherencetotheJidokacomponentofLean,thecombinationof

LeanApplicationDevelopment,LeanApplicationMaintenance,andLean

ApplicationAssetsprovidesthefoundationforLeanApplicationManagement.

SoftwareanalysisandmeasurementiskeytosupportingLeanApplication

Management.Theautomatedanalysisandmeasurementofstructural

qualityofapplicationsiscriticaltotheoverallcostofownership,business

responsiveness,andoperationaldependabilityrequiredtorunaLean

Enterprise.

LeanprocessesworkattheirmostefcientwhenperformedonLeanproducts.

Thebestwaytoreducewasteistodesignitoutoftheapplicationfromthe

beginningbydrivingdesigndecisionsbasedonprioritizedqualitygoals.

Executive Summary (continued)


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I. How Lean Applies to Software

Applications

II. Lean Application Development

III. Lean Application Maintenance

IV. Lean Application Assets

V. Lean Application Management

VI. References

I. How Lean Applies to Software Applications

For the past several years, IT organizations have been exploring the application

o Lean practices pioneered in the Toyota Production System1, 2 or automobile

assembly to the development and maintenance o application sotware. Its ocus

on empowered teams and process streamlining3, 4 has popularized Lean in the

Agile Methods community. However, some o the most powerul benets o

Lean have yet to be ully realized in application development since the Agile

community has ocused more on the just-in-time aspects o Lean rather than onJidoka, the automated early detection and elimination o deects.

Jidoka is one o the two pillars o the Toyota Production System that provides

the oundation or Lean practices (Figure1). Jidoka uses automation to detect

deects early in the production process, as well as their causes so they can be

identied and eliminated. The th principle o what has become known as the

ToyotaWay is to build a culture o stopping to x problems and get quality

right the rst time.5 This principle had a proound eect on the productivity o

automobile manuacturing, and has the potential to transorm the economics o

application development as well.

Figure 1 The Toyota Production System

Contents


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Highlights

An IT organization can establish

Lean Application Management

by automating sotwaremeasurement that supports key

Lean practices.

Jidoka can be implemented in an application portolio through the practices

o Lean Application Management, which ocuses on applying Lean methods

in three areas: Application Development, Application Maintenance, and

Application Assets. In the context o development and maintenance o business

applications, Jidoka involves the use o technologies such as static analysis to

identiy deects when they are added to the source code, so they can be xed

beore being passed on to a later lie cycle phase where their repair is much

more expensive. The sections that ollow describe how an IT organization can

establish Lean Application Management by automating sotware measurement

that supports key Lean practices in each o these three areas.

II. Lean Application Development

The largest opportunity or improving quality and productivity during

application development is by eliminating its largest sources o waste deects

and the rework they cause. In many organizations, 30% to 50% o development

eort is devoted to rework7, 8. These staggering numbers are exacerbated by the

act that deects become 10 times more expensive to x or each major phase o

the sotware lie cycle they slip past. Under these circumstances, productivity is

largely determined by quality.

Beore the Toyota Production System, 25% o the eort in traditional mass

production automobile manuacturing was devoted to xing deects in ully

assembled automobiles at the end o the production line1. Deects that would

have been inexpensive to x during assembly became expensive because

large portions o the car had to be disassembled to make the needed repairs.

Lean practices in the Toyota Production System virtually eliminated rework

areas at the end o production lines, contributing heavily to Leans dramatic

improvements in productivity, quality, and time-to-market over traditional mass

production1.


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Highlights

Structural deects are at

the root o most devastating

operational problems such as

outages, security breaches, data

corruption, and perormance

degradation.

The level o rework in sotware applications is actually worse than that observed

in traditional automobile assembly (Figure2). As in mass production automobile

manuacturing, rework in application development piles up at the end o the

development cycle, causing delays and oten throwing the project into chaotic

thrashing to get the application suciently stable or release. This rework is

maniest in the continuing cycle o testing, recoding deective components, and

then retesting them to nd even more deects.

Figure 2 Comparison of Rework in Traditional Automobile Assembly and Software Development

Why doesnt testing eliminate the rework problem? Modern testing practices

are generally eective in detecting unctional deects deviations rom what

the customer wanted. However, testing is not as eective in detecting the

non-unctional deects that represent structural faws in the construction o the

application9,10,11. These structural deects are dicult to detect through testing

and are requently the most expensive to x because they involve interactions

between multiple tiers o the application, which are oten written in dierent

languages and hosted on dierent platorms. Structural deects are at the root o

most devastating operational problems such as outages, security breaches, data

corruption, and perormance degradation9 10. Static analysis o the structural

quality o business applications augments other quality assurance techniques

such as testing and design inspections to eliminate such deects.


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Highlights

I structural deects escape

into operations, the cost o

correcting them can expand by

an order o magnitude.

There are three main keys to eliminating rework (Figure3) in Lean Application

Development.

Figure 3 How Structural Analysis Contributes to Lean Application Development

1. Detect and remediate deects early: Every component added to the

source code should be analyzed at build time when it is integrated with the

evolving application to see its impact on the system as a whole. Detection

and repair at this point can be an order o magnitude cheaper than i these

structural faws slip into the nal stages o testing, when they are deeply

embedded in the application and a larger portion o the code has to be torn

down, xed, and rebuilt. I structural deects escape into operations where

multiple versions o the sotware are in use, the cost o correcting them can

expand by an additional order o magnitude.

Early detection o deects is especially critical when using Agile. Agile

methods are popular development techniques, but their ast pace and short

release cycles truncate the time available or identiying and remediating

the structural problems remaining in the source code. Although Agile

teams typically rebuild their application daily to integrate newly developed

components, their short schedules do not allow time or evaluating structural

quality at the application level unless the analysis is automated. Agile teams


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Highlights

Organizations that rigorously

apply Lean techniques have

been able to reduce rework by

80% to 90%.

benet rom detecting structural deects early so the most severe ones can be

remediated beore release i a schedule buer provides time or addressing

them. Addressing structural problems prior to release helps prevent waste in

maintenance.

2. Eliminate the causes o deects: When structural quality diagnostics

are coupled with a powerul root cause analysis method such as Orthogonal

Deect Classication12, application teams can identiy the process sourceso structural deects so that their root causes can be eliminated. Typically

an organization will not investigate the causes o every deect, but instead

ocus on those that recur most requently or have the potential or devastating

impact. While early detection minimizes rework, root cause-based process

improvement virtually eliminates it.

3. Train developers continually or deect-ree work: Techniques such

as Pareto charts o structural quality violations identiy the most requent

types o structural deects being injected into applications. The results

can be ed into training activities to help developers avoid these recurring

structural deects in the uture, urther eliminating sources o rework. Some

projects have a ormal launch process or each phase o development, giving

the opportunity to review current issues and the types o errors to be avoided

during the next phase. Developing workfow and a knowledge center to

classiy and guide developers in understanding and remediating structural

deects can raise the bar on the perormance o an application development

organization.

Combinations o these three Lean techniques enabled by sotware analysisand measurement technology can reduce, and in many cases eliminate, the

structural deects that cause the most severe rework in application development.

Organizations that apply these techniques rigorously have been able to reduce

rework by 80% to 90%13, dramatically improving productivity and shortening

time-to-release.


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Highlights

As much as two-thirds o all

maintenance eort can beclassifed as waste resulting

rom poor application design

and coding.

III. Lean Application Maintenance

The two primary sources o waste in application maintenance are rework due to

deects or badly constructed sotware and the staggering time maintenance sta

spend trying to understand what is going on in poorly constructed applications.

Correcting deects requently accounts or as much as one third o all post-

release work, and time spent understanding the code has been shown to account

or as much as hal o all the eort expended by maintenance sta14,15(Figure4).

When the overlap between these two activities is removed, as much as two thirdso all maintenance eort can be classied as waste. Since these sources o waste

result rom poor application design and coding, they must be addressed as a

critical ocus o Lean Application Maintenance.

Figure 4 How Maintenance Staff Spend Their Time15

By identiying structural deects that have a high probability o causing

operational problems and result in maintenance rework, sotware analysis and

measurement provides strong support or Lean Application Maintenance. In

addition, by addressing the changeability o the code beore a release, the time

spent understanding the application can be dramatically reduced in maintaining

subsequent releases. Similarly, by sustaining the quality o the application over

its lietime, IT can avoid degraded applications and costly redevelopments.


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Highlights

Improving the changeability

o an application slashes the

time wasted understanding

unnecessarily complex code.

There are three main keys to eliminating waste (Figure5) in Lean Application

Maintenance.

Figure 5 How Structural Analysis Contributes to Lean Application Development

1. Improve application changeability: The maintenance team can identiy

the most serious structural faws degrading the changeability o an application

using diagnostics rom structural analysis o the application, such as high

coupling among components especially across application layers. Prioritizing

and remediating the most severe changeability problems will reduce the

time that maintenance sta spends trying to trace control and data fows

through the code. Improving the changeability o an application not only

slashes the time wasted on understanding unnecessarily complex code, but

also shortens implementation and verication cycles. As a result, the amount

o unctionality delivered in a single release can be as much as doubled.

In addition, as the ease o changing an application improves, ewer new

deects will be injected into the code during maintenance by eliminating the

misunderstanding o interactions among application layers. Lessons learned

in improving changeability can be used in training developers how to produce

more changeable architectures and code.


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Highlights

Technical debt is the cost o

fxing structural problems that

are not fxed beore release,

but can cause problems in the

uture.

2. Reduce deect-fxing releases: IT organizations must too requently plan

releases devoted primarily to xing deects in the application. The deects

that take the longest to x in maintenance are requently structural, since

such faws are harder to detect through traditional testing. Implementing

sotware analysis and measurement as a component o the development

process helps dramatically reduce the number o structural deects that slip

into operational sotware. By detecting and eliminating these deects beore

release, it is possible to reduce deect xing in maintenance to near zero in

a mature development environment13. The percentage o maintenance eort

devoted to implementing new business unctions is dramatically increased as

the releases o an application devoted to deect xing are eliminated.

3. Sustain lietime quality: To track and sustain the quality o an application

throughout its lietime, sotware analysis and measurement should be

implemented as a standard component o the maintenance process. The

structural quality o many applications degrades continually over a succession

o maintenance releases, to the point that management has to weigh the

tradeo between the increasing time and expense o adding unctionalityversus the cost o redeveloping the application. The quality o an application

can be sustained across its lietime by measuring the status o various

quality attributes during maintenance and remediating the quality problems

created by maintenance actions, eliminating the need or extremely costly

redevelopments.

All three o these aspects o Lean Application Maintenance involve reducing the

technical debt that builds up in applications. Technical debt can be dened as

the cost o xing the structural problems in source code that are not xed beorerelease, but will have to be corrected so they dont cause operational problems

or degrade the maintainability o the application. Technical debt has been

conservatively measured to cost $2.82 per line o code, although the actual cost

based on more realistic assumptions about time to repair deects is easily twice as

high16. Applications appear to dier in their technical debt based on the language

in which they were implemented (Figure6). Structural analysis and measurement

provides executives with a way to measure the opportunity or cost reductions rom

applying the practices o Lean Application Maintenance.


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Highlights

Lean processes work at their

most efcient when perormed

on Lean products.

Figure 6 Technical Debt Measured in Applications by Language16

IV. Lean Application Assets

Lean processes work at their most ecient when perormed on Lean products.

The best way to reduce waste is to design it out o the application rom the

beginning by driving design decisions based on prioritized quality goals.

However, even when designed well, applications age and can become less

responsive and obese with code that is no longer needed, wasting machine

resources. This excess is especially acute in a cloud environment where the

application owner is being charged or both space and processing time.


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Highlights

The most eective way to

eliminate waste is to design it

out o an application rom the

beginning.

There are three keys to creating Lean Applications that eliminate waste (Figure7).

Figure 7 How Waste is Reduced by Creating Lean Application Assets

1. Design or prioritized quality characteristics: The principles o LeanProduct Development and Design or Six Sigma both encourage applications

that are designed with quality priorities in mind. Quality characteristics

such as Robustness, Changeability, Security, and Perormance Eciency

should be prioritized or each application at the beginning o its development.

Architectural and coding decisions should maximize the most important

quality characteristics. These quality characteristics can be measured and

analyzed during both development and maintenance to ensure the quality

o the source code is consistent with its quality priorities. Architectural and

coding lessons rom an IT organizations application experience related to

these characteristics should be captured or consideration when making uture

design and coding decisions, since the most eective way to eliminate waste

is to design it out o the application rom the beginning.

2. Tune the perormance o application code: Poorly designed and coded

applications can waste expensive machine resources by requiring more

processing than necessary. Structural analysis should be applied to detect

poor coding and architectural practices that can dramatically aect processing

requirements and time. Structural problems such as expensive calls inside


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Highlights

An applications code base

should be evaluated at each

release to detect code that is no

longer needed.

loops, ailure to properly index queries, and embedding unctions inside

WHERE clauses can dramatically increase processing loads, requiring the

acquisition o additional machine resources and slowing response times. In

one example, a major multinational nancial services company that started

proactively measuring and remediating perormance problems in their

applications saw processing expenses reduced by roughly $10,000 each

month or a single application. Perormance issues are especially acute in

cloud environments where such problems can result in unexpectedly large

bills.

3. Manage application size: The size o an application should be continually

monitored to ensure its growth is under control and ts within parameters

appropriate or its environment. Automated measurement o code size can

be done in both unction points and lines o code to indicate the amount o

code added, changed, or deleted. When these measures indicate that code

growth exceeds allowable thresholds, corrective actions should be triggered.

Further, the code base should be evaluated at each release to detect code

that is no longer needed. Dead code throughout the application can then bescheduled or removal in an upcoming release. Removing dead code ensures

that application growth and the machine resources needed to support it are

consistent with the business requirements o the application. In addition

to tracking application growth, these size measures will be important or

evaluating maintenance productivity, as will be explained in the next section.

V. Lean Application Management

Establishing a Lean IT organization requires rapid and continual eedback on

the quality o the applications being placed in operation. The combination o LeanApplication Development, Lean Application Maintenance, and Lean Application

Assets provides the oundation or Lean Application Management (Figure8),

a critical step in turning an IT organization into a Lean Enterprise. By aggressively

pursuing the Jidoka component o Lean, the analysis and measurement o

applications provides a critical ingredient required to reduce many sources

o waste associated with application development and maintenance.


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Highlights

Automated analysis and

measurement o structural

quality is critical to run a Lean

Enterprise.

Figure 8 Lean Application Management

The ability to continually monitor application productivity is another critical

component o Lean Application Management. Productivity data provides insight

into the eectiveness o Lean practices at the application, portolio, and delivery

center level. It is important to choose a consistent measure or the numerator

o productivity measures that is, or the size o both the application and the

actual work accomplished during each release. When these size measures are

ully automated, they can be evaluated consistently across all applications

and used or comparisons across an application portolio or between dierent

delivery centers. Consequently, the benets o Lean practices and other

improvement activities can also be assessed at the application, portolio, or

delivery center levels. These analyses provide the insight needed by application

executives to target troubled applications or delivery centers, and allocate the

resources required to optimize their cost structure and service to the business.

Sotware analysis and measurement can be easily integrated into a disciplined

sotware lie cycle process. In an undisciplined environment, diagnostics can

be used to motivate disciplined work by holding developers accountable or

the quality o the applications they produce. In either environment, automated

analysis and measurement o structural quality o applications is critical

to the overall cost o ownership, business responsiveness, and operational

dependability required to run a Lean Enterprise.


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References

1. J.P. Womack, D.T. Jones, & D. Roos (1990). TheCarThatChangedtheWorld. New York: Free Press.

2. J. Womack & D.T. Jones (1996). LeanThinking. New York: Simon & Schuster.

3. M. Poppendieck & T. Poppendieck (2003). LeanSoftwareDevelopment:AnAgileToolkit . Boston: Addison Wesley.

4. M. Poppendieck & T. Poppendieck (2007). ImplementingLeanSoftwareDevelopment. Boston: Addison Wesley.

5. J.K. Liker (2004). TheToyotaWay. New York: McGraw-Hill.

6. J.M. Morgan & J.K. Liker (2006). TheToyotaProductDevelopmentSystem . New York: Taylor & Francis.

7. R. Dion (1993). Process improvement and the corporate balance sheet. IEEESoftware, 10 (4), 28-35.

8. B. Boehm (1987). Increasing sotware productivity. IEEEComputer, 20 (9), 43-57.

9. D. Spinellis (2006). CodeQuality. Boston: Addison Wesley.

10. M.T. Nygard (2007). ReleaseIt. Pragmatic Programmers.

11. D. Jackson (2009). A direct path to dependable sotware. CommunicationsoftheACM, 52 (4), 78-88.

12. R. Chillarege, et al. (1992). Orthogonal Deect Classication: Concept or In-Process Measurement.

IEEETransactiononSoftwareEngineering, 18, (11), 943-956.

13. M.C. Paulk, C.W. Weber, B. Curtis, & M.B. Chrissis (1995). TheCapabilityMaturityModel. Boston: Addison Wesley.

14. D.C. Littman, J. Pinto, S. Letovsky, & E. Soloway (1987). Mental models and sotware maintenance.

JournalofSystemsandSoftware, 7 (4), 341-355.

15. M. Ben-Menachem & G.S. Marliss (2005). IT assets - control by importance and exception:

Supporting the paradigm o change. IEEESoftware, 22 (4), 94-102.

16. J. Sappidi, B. Curtis, & J. Subramanyam (2010). CASTWorldwideApplicationSoftwareQualityStudy2010 .

New York: CAST Research Labs.


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Dr. Bill Curtis

Senior Vice President and Chief Scientist

Bill Curtis is an industry luminary who is responsible or infuencing CASTs

scientic and strategic direction, as well as helping CAST educate the IT market

to the importance o managing and measuring the quality o its sotware. He is

best known or leading the development o the Capability Maturity Model (CMM)

which has become the global standard or evaluating the capability o sotware

development organizations.

Prior to joining CAST, Dr. Curtis was a Co-Founder o TeraQuest, the global leaderin CMM-based services, which was acquired by Borland. Prior to TeraQuest, he

directed the Sotware Process Program at the Sotware Engineering Institute

(SEI) at Carnegie Mellon University. Prior to the SEI he directed research on

intelligent user interace technology and the sotware design process at MCC,

the th generation computer research consortium in Austin, Texas. Beore MCC

he developed a sotware productivity and quality measurement system or ITT,

managed research on sotware practices and metrics at GE Space Division, and

taught statistics at the University o Washington.

Dr. Curtis holds a Ph.D. rom Texas Christian University, an M.A. rom the

University o Texas, and a B.A. rom Eckerd College. He was recently elected a

Fellow o the Institute o Electrical and Electronics Engineers or his contributions

to sotware process improvement and measurement. In his ree time Dr. Curtis

enjoys traveling, writing, photography, helping with his daughters homework, and

University o Texas ootball.


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Europe

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North America

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New York NY 10016t ft

About CAST

CAST is a pioneer and world leader in Sotware Analysis and Measurement, withunique technology resulting rom more than $90 million in R&D investment. CAST

provides IT and business executives with precise analytics and automated sotware

measurement to transorm application development into a management discipline.

More than 650 companies across all industry sectors and geographies rely on CAST

to prevent business disruption while reducing hard IT costs. CAST is an integral part

o sotware delivery and maintenance at the worlds leading IT service providers such

as IBM and Capgemini.

Founded in 1990, CAST is listed on NYSE-Euronext (Euronext: CAS) and serves IT

intensive enterprises worldwide with a network o oces in North America, Europeand India. For more inormation, visit www.castsotware.com.

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Cast WP Lean Application Management Web