No Silver Bullet Essence and Accidents of Software Engineering Frederick P. Brooks, Jr

Readings in Computer Science: Software Engineering

No Silver BulletNo Silver BulletEssence and Accidents of Software EngineeringEssence and Accidents of Software Engineering

Frederick P. Brooks, Jr.Frederick P. Brooks, Jr.

No Silver BulletNo Silver BulletEssence and Accidents of Software EngineeringEssence and Accidents of Software Engineering

Frederick P. Brooks, Jr.Frederick P. Brooks, Jr.

Prepared by Jinzhong NiuPrepared by Jinzhong NiuApril 22, 2023April 22, 2023

Readings in Computer Science: Software Engineering 2NSB

Frederick P. Brooks, Jr.

Kenan Professor of CS atUniv. of North Carolina - Chapel Hill

Achievements IBM OS/360

“The mythical Man-Month”

Honors and Awards A.M. Turing Award, ACM (1999)

National Medal of Technology (1985)


About this paper

Proc. IFIP Congress 1986, Information Processing 86

IEEE Computer, Vol. 20, No. 4, Apr. 1987

The Mythical Man-Month, 2nd Edition, 1995

Software Engineering, edited by Merlin Dorfman and Richard Thayer, Wiley-IEEE Press 1st Edition, 1996 2nd Edition, 2002


What are werewolves and silver bullets?

Werewolf one of the oldest monster legends popular movie topic

Silver bullet the only thing that can kill werewolves

Even a man who is pure at heart,And says his prayers at night,Can become a wolf when the wolfbane blooms,And the moon is full and bright.

-- From “The Wolf Man”


“Essence” and “Accident”

Essence noun, the permanent as contrasted with the

accidental element of being the mental crafting of the conceptual constructs

Accident noun, a nonessential property or quality of an

entity or circumstance; appurtenant(rather than misfortune or occurring by chance)

the implementation process of conceptual constructs


Overview

Software development is werewolf, but there is no silver bullet because of its essential difficulties.

I. What is the nature of software development? (Why does it have to be hard?)

II. Did past breakthroughs solve the problem?

III. Is there any potential solution nowadays?

IV. Will the problem be attacked in the future?


Problem solving strategy

知己知彼，百战不殆。—— 《孙子兵法》

Know your enemy and know yourself; in a hundred battles, you will never be defeated.

--- SUN TZU ON THE ART OF WAR


Why does software engineering have to be hard?

Outside Computer hardware progress is an

exception.

Inside There are essential difficulties which are

hard to be attacked.


Essential difficulties

Complexity

Conformity

Changeability

Invisibility


Essential difficulties: Complexity

A system is usually defined as a collection of components, which interact with one another.

Software is much more complex than any other human construct. The number of elements

The interaction between elements


Essential difficulties: Complexity --- Cont.

A variety of problems are caused.

TechnicalDecrease of reliability, usability, extensibility, safety

ManagerialDifficulty of communication between team membersDifficulty of keeping a clear integrated overview and all

the loose endsDifficulty of personnel turnover due to tremendous

learning and understanding burden


Why high complexity?

Software varies.

A colorful world needs colorful software systems, because

“software has become the dominant technology in many if

not most technical systems. It often provides the

cohesiveness and data control that enable a complex

system to solve problems.” [SwSE, Richard Thayer]

A single piece of software involves high complexity.

High conformity


Essential difficulties: Conformity

Unlike physics where a terrible but invariable complexity exists, software has to conform many human institutions and system interfaces, the number of which is still swelling all the time.

Redesign of the software alone cannot simplify out the complexity.


Essential difficulties: Changeability

Software is constantly subject to pressures for change.

Successful software DOES change frequently.


Essential difficulties: Changeability --- Cont.

Why? Necessity:

Software embodies function, which most feels the pressures of change in a system.

Successful software is hoped to function over time. It is hoped to function in new domains.

Feasibility:Software, pure thought-stuff, is infinitely malleable.


Essential difficulties: Invisibility

Software is invisible in the sense that it is not inherently embedded in space.

Software structure is difficult to visualize in a hierarchical fashion.


Did past breakthroughs solve the problem?

No. What they attacked are accidental difficulties not essence.

Give me some examples! High-level languages Time-sharing Unified programming environments


High-level languages

The development of high-level languages is credited with at least a factor of five in productivity,

concomitant gains in reliability, simplicity, and comprehensibility.

It, however, eliminates only the complexity related to lower level constructs that are not inherent in software.

The level of our thinking about data structures, data types, and operations is steadily rising, but at an ever decreasing rate, and approaches closer and closer to the sophistication of users.


High-level languages --- Cont.

Leve

l of A

bstra

ctio

n

Time

The sophistication level of human

bitsregistersconditionsbrancheschannelsdisks...

int, float, double, …variable, array, record, …for, while, switch, ……

objectclassmessages...


Time-sharing

Time-sharing eliminates the slow turnaround of batch programming, and keeps fresh in mind the grasp of a complex system.

The benefit of time-sharing is to be boundary due to the human threshold of noticeability.


Unified programming environments

Unified programming environments enable related individual tools to work together in an automatic manner. They thus free programmers from the burden of various manual operations.

By its very nature, the fruit is and will be marginal.


Is there any potential solution nowadays?

Ada and other high-level language advances ? Object-oriented programming ? Artificial Intelligence ? Expert Systems ? “Automatic” programming ? Graphical programming ? Program verification ? Environments and tools ? Workstations ?


Ada

Ada, one of the most touted recent development,

not only reflects evolutionary improvements in

language concepts, but indeed embodies features to

encourage modern design and modularization.

Nevertheless, it is just another high-level language

and will not prove to be the silver bullet.


Object-oriented programming

Two orthogonal concepts representing real advances: abstract data types hierarchical types

OO Concepts: encapsulation abstraction inheritance polymorphism dynamic binding


Object-oriented programming --- Cont.

OO represents real advances in the art of building software.

Nevertheless, they remove only accidental difficulties from the expression of the design, rather than the design itself.


Artificial Intelligence

Terminological chaos – Two definitions:

AI-1: The use of computers to solve problems that previously could only be solved by applying human intelligence.

AI-2: The use of a specific set of programming techniques known as heuristic or rule-based programming. (expert system)


Artificial Intelligence --- Cont.

AI advancements facilitate HCI (Human Computer Interface).

However, the hard thing about building software is deciding what to say, not how to express.


Expert Systems

Definition: a program containing a generalized inference engine and a rule

base, takes input data and assumptions, explores the inferences derivable from the rule base, yields conclusions and advice, and explains its results by retracting its reasoning for the user

Advantages: Inference-engine technology is application-independent. The application-peculiar materials are encoded in the rule base in

a uniform fashion, which regularizes the complexity of the application itself.


Expert Systems --- Cont.

User

User Interface

Inference Engine

Input data

Knowledge Base

(Rules, Facts)

advices


Expert Systems --- Cont.

Possible benefits: Expert systems in software engineering field Building software in the way expert systems work

Difficulties How to generate automatically the diagnostic rules

from program-structure specification How to extract expertise and distill it into rule bases


“Automatic” programming

Automatic programming is actually a euphemism for programming with a higher-level language so that a solution could be given more easily.

There are some exceptions which have favorable properties: Relatively few parameters are involved. Many solutions are available. Explicit rules are known to select solutions.

It is hard to generalize such special cases for the ordinary software systems.


Graphical programming

Computer graphics, which has been applied successfully in

other fields, seems to be able to play a role in software

design.

Nothing convincing has ever emerged from this approach.

The flowchart, considered as the ideal program-design medium, is

a very poor abstraction of software structure.

The screens of today are too small to show detailed software

diagrams.

In its nature, software is very difficult to visualize.


Program verification

Program verification seems promising to avoid immense effort upon implementation and testing by eliminating errors in the design phase.

No magic! Verifications are so much work that only a few programs have been

verified.

Verification cannot eliminate errors totally since mathematical proofs can also be faulty.

Specification, the baseline of verification, is usually incomplete and inconsistent.


Environments and tools

We have discussed this issue.


Workstations

More powerful computers surely facilitate software development.

But nowadays time of thinking, instead of waiting for computers' response, is the dominant activity of programmers.Magical enhancement thus cannot be expected.


Will the problem be attacked in the future?

The conceptual components of the task are

now taking most of time.

We must consider those attacks that address

the essence of the software problem.


Will the problem be attacked in the future? --- Cont.

Well, there may be some copper bullets: Buy versus build

Requirements refinement and rapid prototyping

Incremental development

Greater designers


Buy versus build

It is common practice to buy off-the-shelf products nowadays due to

the following reasons:

PC revolution has created many mass markets for software, which,

together with zero replication cost of software, stirred the motivation for

software companies to produce more and better software products.

Applicability of software is enhanced with the generalization of software

tools and the constantly decreasing hardware/software cost ratio.


Requirements refinement and rapid prototyping

It is hardest to decide detailed technical requirements.

Unfortunately even the clients themselves do not

exactly know what they want.

So iterative extraction and refinement of product

requirements are necessary.


Requirements refinement and rapid prototyping --- Cont.

A client cannot specify completely, precisely, and

correctly the exact requirements of a modern software

product before trying some versions of the product.

Rapid prototyping may give clients a first-hand feel of

what the product will be and a check for consistency

and usability.


Incremental development

To develop software that has a comparable complexity as

human brain, a similar process should be followed, i.e.

incremental development.

Advantages:

The approach necessitates top-down design, thus allowing easy

backtracking and detecting fundamental defects as early as

possible.

An always working system stirs enthusiasm.


Greater designers

People is the key factor of solving problems.

Steps have been taken to raise the level of our practice from poor to good. Curricula

Literature

Research organizations


Greater designers --- Cont.

The proposed next step is to develop ways to grow great designers.

Why?

Creative minds present state-of-the-art works, the benefits of which are order-of-

magnitude compared with the average practices.

How?

Identify top designers as early as possible

Assign a career mentor to be responsible for the development of the prospect

Work out a career-development plan for each prospect

Provide opportunities for designers to interact with and stimulate each other



Bullets towards NSB

1. We cannot abstract away the complexity without abstracting away

the essence?

“Divide and conquer” strategy

Is a hierarchical model of software possible?

We cannot always visualize software in hierarchical graphs?

2. The benefit of time-sharing is boundary?

3. The hardest single part of building a software system is deciding

precisely what to build?

How about design?

Documents

No Silver Bullet Essence and Accidents of Software Engineering Frederick P. Brooks, Jr