From the classics like Autodesk, to more specialized programs like SolidWork

Product engineers and designers require specialized tools to create accurate 3D models and technical drawings. To do this, there are numerous design software that offer advanced features and precision in detail.

Now, which of all will be the best? It depends on the needs and what the engineer is looking for. In this article, we will introduce 10 design software for engineers worth knowing. From the classics like Autodesk, to more specialized programs like SolidWorks or CATIA, each software has its own strengths and unique features.



Image: Housepaper.com


When we talk about design software applied to engineering and architecture, this is a fair and necessary title. Knowledge of the Autodesk suite has several benefits, including the possibility of using 2D drawings as a design basis, integrating specialties such as electricity, plumbing or mechanics, and the fact that its programs are the most widely used and standardized on the market.



CATIA is software that enables design engineers to model products and objects based on their behavior in real life, allowing them to design in the age of experience. Its main use is for solid modeling xxx gratuit.


Its interactive environment for numerical calculation, visualization, and code programming makes it very popular among these professionals. Among the benefits that Matlab offers are the ability to perform numerical calculations using mathematical functions, the rapid execution of vector and matrix calculations, programming, data modeling, and the possibility of sharing the results in the form of graphs or full reports.


Rhino is a well-known and versatile 3D modeling software that is available on Windows and Mac. It is used by design engineers and offers a wide range of features, including the ability to create, edit, analyze, document, render, animate, and translate. NURBS curves, surfaces and solids, point clouds and polygon meshes without limitations.

Rhino also offers free-form 3D modeling tools, is compatible with other design software, and can read and repair very challenging IGES files and meshes.



As a portfolio of affordable and easy-to-use software tools, it addresses all aspects of the product design and development process for design engineers. This includes 3D design, simulation, manufacturing, design management, and more. With its synchronous technology, Solid Edge combines the speed and simplicity of direct modeling with the flexibility and control of parametric design.


MechDesigner is specialized in the design and analysis of machines and products that involve moving parts, allowing to simulate the movement of these parts in a precise and fluid way.

With MechDesigner, you can ensure designs move correctly and precisely, even if the machine or product has complex motions, multiple interacting mechanisms, cams, gears, or CAD-designed parts.


We close this list with a very versatile option that helps designers increase the speed and productivity of their work. With it, you can improve design visualization and communication, eliminate interference issues, and verify functionality and performance without the need for physical prototypes.

SolidFace also has built-in 3D direct modeling and 2D/3D motion simulation modules.






In recent years, and especially since the Covid-19 pandemic, the world of work has gone digital. Thanks to the rise of home-office and telecommuting jobs, many people are also considering learning to program, and one of the most popular, and useful, languages is Python.

Even if being a programmer is not your goal, the truth is that all professions are undergoing a transformation in which apps and Artificial Intelligence are becoming more prominent. That is why knowing even the basics of programming can be very useful.


According to data from the Inter-American Development Bank (IDB), it is estimated that by 2025 Latin America will need 1.2 million software developers to meet the demand. If you want to get started in this world, you can learn here everything you need to know about Python, including its uses and how much time you need to learn how to use it.


Python is a programming language that has many uses, from web applications to machine learning. A programming language, in turn, is the tool programmers have to write a series of instructions (algorithms) to control the purpose of a computer system.

Although there are several programming languages (FORTRAN, Ada, Perl, ALGOL, ASP, BASIC, Java, JavaScript, and many more), Python is one of the most popular for several reasons. One is that its software is free to download and has the advantage of integrating well with all types of systems.

On the other hand, both professional programmers and beginners take advantage of the fact that it is one of the most “friendly” languages, as its syntax is very similar to that of English. This also allows shorter programs to be written than in other languages.

Also, besides having a library with different reusable codes for many tasks (which makes work easier), Python is also a good base for developers to learn other programming languages such as Java, for example xxx porno.



Another advantage of Python is that it is widely used in different sectors, from education and communications to science and medicine. Among its uses are web development, computer vision and image processing, video game development, robotics, autonomous vehicles, among others.


However, one of the areas where it is most used is in data science, since thanks to its visualisation library it is possible to create graphs and visual representations. Another interesting application of Python is in machine learning, which is the area of computer science in which systems are created that are capable of learning by themselves.



Also, as Python is one of the easiest languages to learn, it is used around the world in introductory computer science and programming courses. For example, MIT is one of the institutions that uses it.

Finally, Python also has interesting uses in medicine. These include:
making clinical diagnoses based on medical records and symptoms, analysing data, creating models for developing new drugs, and so on.


According to the educational website SuperProf, it is estimated that learning Python can take, on average, between six months and a year. That is, of course, if you follow a system of constant study, where you work on a daily basis with good training. In this time, you could be performing basic functions, as well as web applications or platforms.

There are several options for learning Python. The one most people use is the self-taught style, as there are plenty of resources on the Internet to get you started. However, this requires discipline and structure. If self-study is not your thing, there are also courses (even university courses) that may be an option.




Top 10: Scientific Predictions that Failed

Top 10: Scientific Predictions that Failed

We trust science; We owe everything to their great advances. However, he is not always successful with his ads. Here we will list the biggest failed predictions of science. Possibly, you know some and others will amaze you.


Times when science failed with its calculations

1.Comet Halley Would Destroy the Earth

Comet Halley passes through the earth’s vicinity about every76 years. Its passage in 1910 was predicted to be too close to destroy the earth either via the poisonous gas (it was thought to contain) or by a celestial collision. A global panic ensued, fueled by the media and alarming headlines. Packaged air became a fast-selling commodity, and a congregation in Oklahoma attempted to martyr a virgin to ward off the impending catastrophe. 

Although the earth passed through a section of comet Halley’s tail, there was no apparent effect porno français.


2.Excessive Smoking Hardly Causes Lung Cancer

W.C. Heuper of the Nation Cancer Institute (1954) once claimed that excessive smoking does not have a significant role, if any, in the production of lung cancer. Contrary to his statement, smoking accounts for 90% of lung cancer deaths in men and 80% in women. From 2005 to 2010, about 130659 Americans succumbed to smoking-related cancer every year. On average, 103659 Americans per year succumbed to smoking-related cancer between 2005 and 2010.


3.Humans Have Won Against Infectious DiseasesWe Should Stop Investing In Science

Times when science failed with its calculations

Dr. William H. Steward, while delivering a message of hope to the white house in 1967, said, “It’s time to close the books on infectious diseases, declare the war against pestilence won…..” He encouraged channeling resources towards chronic conditions like cancer and heart disease. While plagues like polio, measles, and smallpox are no longer a grave threat, the world still faces serious and sometimes novel infectious diseases like Ebola, bird flu, SARS, and MERES, to mention a few that can potentially kill populations.


4.Pasteurization is Absurd

Pierre Pachet, a professor of physiology at Toulouse in 1872, dismissed the theory of germs by Louis Pasteur. He went on to claim that the theory was ridiculous fiction. Today, humans still use pasteurization to kill germs in drinks and foods.


5.Humans Cannot Obtain Nuclear Energy

In 1932, Albert Einstein communicated his skepticism of nuclear power. He said that there was no slight indication of obtaining nuclear energy and that its accomplishment would mean shattering the atom at will. December 2nd, 1942 (ten years later), was the year of history – the initiation of the first human-made self-sustaining nuclear chain reactor.

6.We Should Stop Investing In Science

We Should Stop Investing In Science

The US senator Simon Cameron while commenting on the Smithsonian Institution in 1901, said he was tired of all things called science. He added that the government had been spending millions on science for the past few years, and that should stop. Today, science remains a crucial resource in various aspects of life, including health, agriculture, food, art, industries, and more.


7.Limited Astronomical Knowledge

In 1888, the astronomer Simon Newcomb predicted that humans were approaching the limit of their knowledge concerning astronomy. Since his remarks, the astronomers have attained numerous astronomical milestones, including the development of the Theory of Relativity, the discovery of Pluto, and the realization of the existence of galaxies beyond the Milky Way. Furthermore, men walked the moon and even built the International Space Station.


8.Lifestyle Choices Cause AIDS

Peter Duesberg, a molecular biologist cum professor at the University of California, is a key figure in the AIDS denialism movement. In 1988, Peter suggested that HIV did not cause AIDS, but lifestyle factors did. With this debunked, we know that the HIV virus causes AIDS.


9.X-Ray is a Hoax

In 1883, the President of the Royal Society, Lord Kelvin, claimed that X-rays would prove to be a hoax. He later on changed and had an X-ray performed on his left hand in 1896. X-rays remain an important diagnostic tool in modern medicine. In 2009, the X-ray machine was termed the most important scientific invention during a poll to mark the London Science Museum.


10.Surgeons Will Never Perform Brain, Abdomen, and Chest Surgeries

In 1870, the surgeon to Queen Victoria, Sir John Eric Ericson, predicted the impossibility of achieving chest, brain, and abdomen surgeries. He said, “The abdomen, the chest, and the brain will forever be shut from the intrusion of the wise and humane surgeon.” As one of the shapers of modern surgery, Sir John probably misjudged the future advances in his field.




COVID-19 Changes Your Brain

Patients suffering from Covid-19 are experiencing a range of effects on their brains, including loss of smell and taste, confusion, and life-threatening strokes. This has attracted the attention of researchers to study the impact of Covid-19 infection on the brain.

A comprehensive molecular study of the brain tissue from people that succumbed to covid-19 proves that SARS-CoV-2 causes profound molecular alterations in the brain, notwithstanding the absence of the virus in the brain mass. The changes the virus leaves in the brain indicate intense inflammation and disrupted brain circuits observed in Alzheimer’s or other neurodegenerative conditions. the lead author in the study, Tony Wyss-Coray, professor in the School of Medicine, confirmed the findings xnxx.

Up to a third of persons infected with SARS-CoV-2 exhibited brain symptoms like brain fog, fatigue and memory problems. Many individuals experience such symptoms long after recovering from the virus infection. However, the virus’ mechanism of causing these symptoms and its molecular effects on the brain are still unclear. The researchers deployed singer-cell RNA sequencing to study brain tissue samples from 8 patients who succumbed to the covid-19 virus and 14 control samples from those who died of other causes. Surprisingly, the study found significant changes in the brain cells and different types of cells (immune, nerve and support cells) in the brain. Across cell types, covid-19 effects resemble those observed in chronic brain illnesses and exist in genetic variants related to cognition, depression, and schizophrenia.

From the research, viral infections tend to trigger inflammatory reactions throughout the body. These inflammatory responses may cause inflammatory signaling across the blood-brain barrier and consequently trigger neuroinflammation in the brain. The study results may explain the brain fog, fatigue alongside other neurological and psychiatric symptoms linked to covid. 

A more extensive study (with nearly 800 participants) compared brain scans from the same people before and after infection with SARS-CoV-2. The scans showed tissue abnormalities and loss of gray matter in people who had the covid disease than in those who did not. The changes mainly affected the areas of the brain related to smell. Neurological experts, commenting on the publication in the journal of nature, said the findings were valuable and unique. They cautioned that the consequences of the changes are not clear. The results do not necessarily imply that people may experience long-lasting damage or that the observed changes profoundly affect memory, thinking, and other functions.


The brain images and cognitive scores of persons with SARS-CoV-2 exhibited changes between the two scans compared to those in the control group. The differences were more significant in older participants. While not all who became infected with the virus showed these differences, the group that had a prior infection on average had:

  • A diminished gray matter mass in the brain areas associated with the sense of smell (parahippocampal gyrus and orbitofrontal cortex)
  • More tissue damages in the parts linked with the primary olfactory cortex (also connected with the sense of smell)
  • A decline in entire brain volume and increase in the volume of cerebrospinal fluid
  • A significant drop in the ability to perform complex tasks – linked to atrophy in the area of the brain associated with cognition

According to the team of researchers, the potential mechanisms by which the virus infection might affect the brain include;

  • A reduced sensory input particularly related to smell
  • Immune reactions or neuroinflammation
  • Viral infections to the brain cells



Potential Life-Supporting Planet Found Orbiting a Dying Star

Astronomers have spotted a ring of planetary fragments revolving around a white dwarf star that’s 117 light-years away, suggesting there could be a habitable zone that can sustain life and waters. If this is confirmed, it would be the first time such a planet is seen orbiting a dying sun. White dwarfs are dead stars that have consumed all of their hydrogen fuel. All the stars are expected to get to this point in billions of years. Therefore, the findings will also give us a glimpse of our solar system’s future when the sun starts wearing away. 


A Novel Discovery 

The study was steered by Professor Jay Farihi, based at the University College London, who stated that this was the first time space scientists had detected a planetary body in the inhabitable zone of a white dwarf. The possible planet is estimated to be 60 times closer to the star than Earth is to the sun. 

In this new study, the researchers measured light from a white dwarf in the Milky Way (The WD1054-226). They analyzed information from the ground and space-based telescopes. The scientists were shocked to find marked slopes of light that correspond to 65 uniformly spaced clouds of planetary debris revolving around the star every 25 hours porno.


The Uncovered Mysteries 

The scientists noticed that the star’s light would dim every 23 minutes, implying that a nearby planet kept the stars in a particular arrangement. Professor Farihi explained that the moon-sized structures they observed were irregular and dusty. They appeared comet-like and not as spherical bodies. 

The team does not have a solid explanation for the absolute regularity, one passing in front of the star at regular intervals of 23 minutes. However, one of the explanations would be that the bodies are kept in an evenly spaced pattern by the gravitational influence of a nearby planet. Without this influence, collisions and friction would cause the structures to disperse and lose the observed precise regularity. 

The observation, known as shepherding, has been observed in Neptune and Saturn. The gravitational pull of these planets’ moons helps establish a stable ring structure revolving around these planets. The likelihood of having a world in the habitable zone is exciting and unexpected. 


A Long Way to Go

Although the researchers found exciting information, they believe that more data is required to confirm the presence of a planet. At the moment, they cannot observe the globe directly. Therefore, the confirmation can only be made by comparing computer models with further observations of the star and orbiting debris. 

Experts believe that any planet revolving around the white dwarf can sustain water and life. They estimate the zone to be inhabitable for at least two billion years from now. Over 95% of all stars will eventually become white dwarfs, including the sun. Considering that our sun will become a dwarf within a few billion years to come, this study offers crucial insight into our solar system’s future. Kudos to Professor Jay Farihi and his team.




The Cost Challenges of System of Systems

The Department of Defense (DoD) has migrated from a platform-based acquisition strategy to one focused on delivering capabilities. Instead of delivering a fighter aircraft or an unmanned air vehicle, contractors are now being asked to deliver the right collection of hardware and software to meet specific wartime challenges. This means that much of the burden associated with conceptualizing, architecting, integrating, implementing, and deploying complex capabilities into the field has shifted from desks in the Pentagon to desks at Lockheed Martin, Boeing, Rockwell, and other large aerospace and defense contractors.

In “The Army’s Future Combat Systems’ [FCS] Features, Risks and Alternatives,” the Government Account-ing Office states the challenge as:

…14 major weapons systems or platforms have to be designed and integrated simultaneously and within strict size and weight limitations in less time than is typically taken to develop, demonstrate, and field a single system. At least 53 technologies that are considered critical to achieving critical performance capabilities will need to be matured and integrated into the system of systems. And the development, demonstration, and production of as many as 157 complementary systems will need to be synchronized with FCS content and schedule. [1]
The planning, management, and execution of such projects will require changes in the way organizations do business. This article reports on ongoing research into the cost challenges associated with planning and executing a system of systems (SOS) project. Because of the relatively immature nature of this acquisition strategy, there is not nearly enough hard data to establish statistically significant cost-estimating relationships. The conclusions drawn to date are based on what we know about the cost of system engineering and project management activities in more traditional component system projects augmented with research on the added factors that drive complexities at the SOS level.

The article begins with a discussion of what an SOS is and how projects that deliver SOS differ from those projects delivering stand-alone systems. Following this is a discussion of the new and expanded roles and activities associated with SOS that highlight increased involvement of system engineering resources. The focus then shifts to cost drivers for delivering the SOS capability that ties together and optimizes contributions from the many component systems. The article concludes with some guidelines for using these cost drivers to perform top-level analysis and trade-offs focused on delivering the most affordable solution that will satisfy mission needs.

Related Research
Extensive research has been conducted on many aspects of SOS by the DoD, academic institutions, and industry. Earlier research focused mainly on requirements, architecture, test and evaluation, and project management [2, 3, 4, 5, 6, 7, 8]. As time goes on and the industry gets a better handle on the technological and management complexities of SOS delivery, the research expands from a focus on the right way to solve the problem to a focus on the right way to solve the problem affordably. In the forefront of this cost-focused research is the University of Southern California’s Center for Software Engineering [9], the Defense Acquisition University [10], Carnegie Mellon’s Software Engineering Institute [11], and Cranfield University [12].

What Is an SOS?
An SOS is a configuration of component systems that are independently useful but synergistically superior when acting in concert. In other words, it represents a collection of systems whose capabilities, when acting together, are greater than the sum of the capabilities of each system acting alone.

According to Mair [13], an SOS must have most, if not all, of the following characteristics:

Operational independence of component systems.
Managerial independence of component systems.
Geographical distribution.
Emergent behavior.
Evolutionary development processes.
For the purposes of this research, this definition has been expanded to explicitly state that there be a network-centric focus that enables these systems to communicate effectively and efficiently.

Today, there are many platforms deployed throughout the battlefield with limited means of communication. This becomes increasingly problematic as multiple services are deployed on a single mission as there is no consistent means for the Army to communicate with the Navy or the Navy to communicate with the Air Force. Inconsistent and unpredictable means of communication across the battlefield often results in unacceptable time from detection of a threat to engagement. This can ultimately endanger the lives of our service men and women.

How Different Are SOS Projects?
How much different is a project intended to deliver an SOS capability from a project that delivers an individual platform such as an aircraft or a submarine? Each case presents a set of customer requirements that need to be elicited, understood, and maintained. Based on these requirements, a solution is crafted, implemented, integrated, tested, verified, deployed, and maintained. At this level, the two projects are similar in many ways. Dig a little deeper and differences begin to emerge. The differences fall into several categories: acquisition strategy, software, hardware, and overall complexity.

The SOS acquisition strategy is capability-based rather than platform-based. For example, the customer presents a contractor with a set of capabilities to satisfy particular battlefield requirements. The contractor then needs to determine the right mix of platforms, the sources of those platforms, where existing technology is adequate, and where invention is required. Once those questions are answered, the contractor must decide how best to integrate all the pieces to satisfy the initial requirements. This capability-based strategy leads to a project with many diverse stakeholders. Besides the contractor selected as the lead system integrator (LSI), other stakeholders that may be involved include representatives from multiple services, Defense Advanced Research Projects Agency, prime contractor(s) responsible for supplying component systems as well as their subcontractors. Each of these stakeholders brings to the table different motivations, priorities, values, and business practices – each brings new people management issues to the project.

Software is an important part of most projects delivered to DoD customers. In addition to satisfying the requirements necessary to function independently, each of the component systems needs to support the interoperability required to function as a part of the entire SOS solution. Much of this interoperability will be supplied through the software resident in the component systems. This requirement for interoperability dictates that well-specified and applied communication protocols are a key success factor when deploying an SOS. Standards are crucial, especially for the software interfaces. Additionally, because of the need to deliver large amounts of capability in shorter and shorter timeframes, the importance of commercial off-the-shelf (COTS) software in SOS projects continues to grow.

With platform-based acquisitions, the customer generally has a fairly complete understanding of the requirements early on in the project with a limited amount of requirements growth once the project commences. Because of the large scale and long-term nature of capability-based acquisitions, the requirements tend to emerge over time with changes in governments, policies, and world situations. Because requirements are emergent, planning and execution of both hardware and software contributions to the SOS project are impacted.

SOS projects are also affected by the fact that the hardware components being used are of varying ages and technologies. In some cases, an existing hardware platform is being modified or upgraded to meet increased needs of operating in an SOS environment, while in other instances brand new equipment with state-of-the-art technologies is being developed. SOS project teams need to deal with components that span the spectrum from the high-tech, but relatively untested to the low-tech, tried-and-true technologies and equipment.

Basically, a project to deliver an SOS capability is similar in nature to a project intended to deliver a specific platform except that overall project complexity may be increased substantially. These complexities grow from capability-based acquisition strategies, increased number of stakeholders, increased overall cost (and the corresponding increased political pressure), emergent requirements, interoperability, and equipment in all stages from infancy to near retirement.

New and Expanded Roles and Activities
Understanding the manifestation of these increased complexities on a project is the first step to determining how the planning and control of an SOS project differs from that of a project that delivers one of the component systems. One of the biggest and most obvious differences in the project team is the existence of an LSI. The LSI is the contractor tasked with the delivery of the SOS that will deliver the capabilities the DoD customer is looking for. The LSI can be thought of as the super prime or the prime of prime contractors. He or she is responsible for managing all the other primes and contractors and ultimately for fielding the required capabilities. The main areas of focus for the LSI include:

Requirements analysis for the SOS.
Design of SOS architecture.
Evaluation, selection, and acquisition of component systems.
Integration and test of the SOS.
Modeling and simulation.
Risk analysis, avoidance, and mitigation.
Overall program management for the SOS.
One of the primary jobs of the LSI is completing the system engineering tasks at the SOS level.

Focus on System Engineering
The following is according to the “Encyclopedia Britannica”:

“… system engineering is a technique of using knowledge from various branches of engineering and science to introduce technological innovations into the planning and development stages of systems. Systems engineering is not as much a branch of engineering as it is a technique for applying knowledge from other branches of engineering and disciplines of science in an effective combination. [14]
System engineering as a discipline first emerged during World War II as technology improvements collided with the need for more complex systems on the battlefield. As systems grew in complexity, it became apparent that it was necessary for there to be an engineering presence well versed in many engineering and science disciplines to lend an understanding of the entire problem a system needed to solve. To quote Admiral Grace Hopper, “Life was simple before World War II. After that, we had systems [15].”

With this top-level view, the system engineers were able to grasp how best to optimize emerging technologies to address the specific complexities of a problem. Where an electrical engineer would concoct a solution focused on the latest electronic devices and a software engineer would develop the best software solution, the system engineer knows enough about both disciplines to craft a solution that gets the best overall value from technology. Additionally, the system engineer has the proper understanding of the entire system to perform validation and verification upon completion, ensuring that all component pieces work together as required.

Today, a new level of complexity has been added with the emerging need for SOS, and once again the diverse expertise of the system engineers is required to overcome this complexity. System engineers need to comprehend the big picture problem(s) whose solution is to be provided by the SOS. They need to break these requirements down into the hardware platforms and software pieces that best deliver the desired capability, and they need to have proper insight into the development, production, and deployment of the systems to ensure not only that they will meet their independent requirements, but also that they will be designed and implemented to properly satisfy the interoperability and interface requirements of the SOS. It is the task of the system engineers to verify and validate that the component systems, when acting in concert with other component systems, do indeed deliver the necessary capabilities.

Large Software Systems. Back to Basics

Today when we launch a software project, its likelihood of success is inversely proportional to its size. The Standish Group reports that the probability of a successful software project is zero for projects costing $10 million or more [1]. This is because the complexity of the problem exceeds one person’s ability to comprehend it. According to The Standish Group, small projects succeed because they reduce confusion, complexity, and cost. The solution to the problem of building large systems is to employ those same techniques that help small projects succeed—minimize complexity and emphasize clarity.
The goals, constraints, and operating environment of a large software system, along with its high-level functional specification, describe the requirements of the systems. Assuming we have good requirements, we can decompose our system into smaller subsystems. Decomposition proceeds until we have discrete, coherent modules. The modules should be understandable apart from the system and represent a single idea or concept. When decomposition is finished, the modules can be incorporated into an architecture.
Frederick P. Brooks said that the conceptual integrity of the architecture is the most important factor in obtaining a robust system [2]. Brooks observed that it can only be achieved by one mind, or a very small number of resonant minds. He also made the point that architectural design must be separated from development. In his view, a competent software architect is a prerequisite to building a robust system.
An architecture is basically the framework of the system, detailing interconnections, expected behaviours, and overall control mechanisms. If done right, it lets the developers concentrate on specific module implementations by freeing them of the need to design and implement these interconnections, data flow routines, access synchronisation mechanisms, and other system functions. Developers typically expend a considerable amount of energy on these tasks, so not doing them is a considerable savings of time and effort [3].
A robust architecture is one that is flexible, changeable, simple yet elegant. If done right and documented well, it reduces the need for interteam communication and facilitates successful implementation of complex modules. If done well, it is practically invisible; if done poorly, it is a never-ending source of aggravation, cost, and needless complexity.
Architecture flows from the requirements and the functional specification. The requirements and functional specification need to be traced to the architecture and its modules, and the modules in the architecture should be traced to the requirements and functional specification. The requirements must necessarily be correct, complete, unambiguous, and, where applicable, measurable. Obtaining requirements with these qualities is the responsibility of the architect. It must be his highest priority. He does this by interacting closely with the customers and domain experts. If necessary, he builds prototypes to validate and clarify the requirements The architect acts as the translator between the customers and the developers. The customers do not know how to specify their needs in the unambiguous language that developers need, and the developers do not always have the skills to do requirements analysis.
The architect communicates his desires to the developers by specifying black-box descriptions of the modules. Black boxes are abstract entities that can be understood, and analyzed independently of the rest of the system. The process of building black-box models is called abstraction. Abstraction is used to simplify the design of a complex system by reducing the number of details that must be considered at the same time, thus reducing confusion and aiding clarity of understanding [4]. For safety-critical, military-critical, and other high-integrity systems, black boxes can be specified unambiguously with mathematical logic using formal methods. Supplemented with natural language descriptions, this is probably the safest way to specify a system. It is usually more expensive and time consuming, as well. In the future, however, all software architects should know how to mathematically specify a module.
A robust architecture is necessary for a high-quality, dependable system. But it is not sufficient. A lot depends on how the developers implement modules handed to them by the architect.
The Rest of the Solution
Developers need to build systems that are dependable and free from faults. Since they are human, this is impossible. Instead they must strive to build systems that minimize faults by using best practices, and they must use modern tools that find faults during unit test and maintenance. They should also be familiar with the concepts of measuring reliability and how to build a dependable system. (A dependable system is one that is available, reliable, safe, confidential, has high integrity, and is maintainable [5].) In order for the system to be dependable, the subsystems and modules must be dependable.
Fault prevention starts with clear, unambiguous requirements. The architect should provide these so the developer can concentrate on implementation. If the architecture is robust, the developer can concentrate on his particular module, free of extraneous details and concerns. The architect’s module description tells the developer what to implement, but not how to implement it. The internals of the implementation are up to him. To ensure dependability, the developer needs to use sound software engineering principles and best practices, as these are his chief means of of minimizing complexity. Two best practices are coding standards and formal inspections.
Coding standards are necessary because every language has problem areas related to reliability and understandability. The best way to avoid the problem areas is to ban them, using an enforceable standard. Les Hatton describes why coding standards are important for safety and reliability and how to introduce a coding standard [6]. A key point he stresses is to never incorporate stylistic information into the standard. It will be a never-ending source of acrimony and debate. Such information, he says, should be placed in a style guide. Coding standards can be enforced with automatic tools that check the code, and by formal inspections. The benefits of formal inspections for defect prevention are well-known and well-documented. They are also invaluable for clarifying issues related to the software.
Developers need to measure their code to ensure its quality. This provides useful feedback to the developer on his coding practices, and it provides reassurance to the system’s acquirers and users. Many static metrics can be used to assess the code. Among these are purity ratio, volume, functional density, and cyclomatic complexity. As a doctor uses a battery of tests to gauge a person’s health, relying on more than one metric and covering all his bases, a developer using static analysis tools can do the same [7].
A good metric, for example, is cyclomatic complexity. A large value is a sign of complex code, which may be an indication of poor thought given to the design and implementation. It is also a sign that the code will be difficult to test and maintain.
Fault detection by proper unit testing is vitally important. To be done right, it requires the use of code coverage and path analysis tools. Unfortunately, this type of testing is usually overlooked. Many managers say they cannot afford them. Somehow, though, they can afford to fix the problems after the software has been fielded. This is penny-wise and pound-foolish. It is axiomatic that fixing software faults after the code has been deployed can be up to 100 times more expensive than finding and fixing the during development [8].
Besides path analysis and code coverage tools, automatic testing tools should be used. Human testers cannot hope to match the computer on indefatigability or thoroughness. In large systems, if testing is not automated, it is not done, or done rarely. For example, regression testing, used in systems undergoing modification and evolution, is essential to ensure that errors are not injected into code undergoing change, a very common problem in complex systems. Without automation, the process is onerous and time consuming. It rarely gets done, if at all.
Developing quality code is not simple or easy. It requires discipline and rigor, state-of-the-art tools, and enlightened managers willing to support developers by paying up-front costs, such as giving developers more time to automate and test their code. Developers take pride in their work. When they get the support they need, they know that their managers want them to produce quality code. This makes the work satisfying and rewarding.
Managing and limiting complexity and promoting clarity is fundamental to developing large software systems. The key ingredient is a robust architecture. The conceptual integrity of the  architecture, its elegance and clarity, depends on a single mind. Developers build upon the architecture and ensure its robustness by rigorous application of basic software engineering principles and best practices in their code development.