Articles related to software development, agile methods and FinTech.


Why Robo?

Why Robo?

With the advent of Automated Digital Wealth Management options (aka robo advisers), the conventional wealth management industry is facing perhaps its most tumultuous threat since low-cost online stock trading emerged in the mid 1990’s

  • The Millennial generation’s predisposition to “do-it-yourselfthrough-an-app”
  • Availability of highly credible digital wealth management options
  • Providing scalable advice
  • Suffering in the comparative shift in appetite towards ETFs the mutual fund business, and other passive investment vehicles in particular seems farther threatened since most of the solution providers by digital wealth management solutions use ETFs as their underlying investment vehicles
  • At the absolute minimum, all wealth supervisors should be highly focused on “digitizing” their businesses as consumers of all ages and demographics will expect an “Amazon and Uber – from all of their financial service providers like expertise that is ”
  • Fintech disruption in all areas of finance and investments (online banking, big data, AI, sentiment analysis)
  • New consumer brands are appearing in the digital wealth management sector (such as Betterment, Wealthfront and Personal Capital) while conventional companies are striking back by either offering their own in house options (including Charles Schwab and Vanguard) or partnering or getting to speed time to market


As a wealth advisory service, robo-advisors have already been growing in popularity for recent years. Low minimums, low fees and the guarantee of sound yields, attract new investors, particularly millennials.
Now while this kind of service created considerable asset increase at first, that increase has leveled off. Although the usage of robo-advisors continues, a tendency that is more comprehensive is replacing the service, with financial advisors using computer programs to help them in managing customer accounts and offering investment advice.
There stays a comparatively small section of investors for whom robo-advice and most of the demands can match with.
The truth is, the popularity of robo-advisers, and the lessons learned from their execution, point to online brokerages, a fresh manner advisors and other financial institutions can do business. These investment and advice suppliers obtain or can construct a platform offering much more than just advice that is digital. It can rather power an environment where a personalized investment software is successfully provided through call center, advisors and digital stations, determined by the route that is best, together with client setting.
Call it an automated advice platform—a package of applications that can direct investment choices and more, from tax-loss harvesting and rebalancing to predictive analytics and data mining, helping the adviser and business socialize with the customer through the route that is most suitable. This type of system drives cost savings and efficiencies, together with an increased customer experience.
You’ll find choices and many variables that businesses should consider when planning and installing these systems.


The financial advisory business was limited and included much more manual task by now’s standards, in and to whom.
advice delivery was ineffective and slow, along with exclusive (largely limited to rich or well-off investors). Eventually, it was not difficult for fiscal preparation to become disconnected from your real execution of the investment advice.
When technology increased rate and consumers needed immediate info delivered at their convenience and increased transparency these shortcomings became clear. Many financial advisors, working with insufficient and ageing applications, cannot fulfill with those requirements.

Robo-advisors arrive

Robo- prejudices that are behavioral could be eliminated by advisors and manage regular account maintenance while conserving prices—so the pitch went.
Some of the early robo-advisors came like Betterment and Wealthfront from pure play robo companies. Well-recognized product producers like Vanguard, BlackRock and Schwab got into the space, also. Conventional advisory companies, also as independent broker dealers and custodians, embraced automated investment technologies, looking to attract younger customers (nearly 40% of millennials are thinking about robo-advice) and smaller accounts, together with maintain their peers. Still nowadays, we see niche-oriented robo-companies like WorthFM coming to market, as businesses that are other add artificial intelligence with their platforms.
By investing in a automated platform that is advisory, companies can construct and power an omni-station surroundings—one where advice that is customized is delivered through digital versions, call center and adviser.


Growth rates have dropped, and average earnings per robo-advisors customer is decreasing, which doesn’t bode well for his or her long term prognosis.
If the portfolio is simply an apportionment of various ETFs that are passive, then it’s not more expensive to buy through a self directed account.
The robo-adviser also leaves no selection —once the asset allocation is discovered, it’s impossible to change investments. Other criticisms of pure robo-advisors are that they’ve yet to work during a long bear market or downturn (remaining invested is essential to the investing theory), and it’s not clear how they’ll manage occasions like post-retirement drawdowns. And, while many younger investors are wonderful with fiscal advice that is only digital, many others aren’t. Additionally, many facets of the fiscal advice relationship continue to demand human intervention, like tax strategies and estate planning.
Many of the notions that drove the adoption of robo-advisers in the first place stay sound, and advisers and companies have a prime chance to employ robo-advisory technology more generally across practices and their company. Really, robo- alternatives that are advisory will continue to develop to address these shortcomings, placing pressure that is on-going on advisory businesses that are conventional. By investing within an platform that is advisory that is automated, companies can construct and power an omni-station surroundings—one where advice that is customized is delivered through call center, advisors and digital versions.

By investing in a platform that is advisory that is automated, companies can assemble and power an omni-station surroundings—one where advice that is customized is delivered through digital versions, call center and adviser.
In this hyper-linked information age, a large proportion of investors, no matter account size want professional advice. The fiscal advisory businesses that can best provide this advice—in a sense which is still rewarding to the business, and in the quantity, frequency and format the customer needs —will function as ones that prosper and live. There are lots of newer “tumultuous” suppliers which are using innovative methods to deliver this advice that is professional, such as call centre and the only on-line -based advisory business Personal Capital. This places those conventional advisory companies that manage to completely comprehend their present and prospective customers at a clear edge and to develop their abilities. Companies must have the capacity to serve customers in just how they need. Technology is critical to provide the customer experience that is appropriate, fulfilling the customer’s needs consistently and economically. But, the real innovators are the ones that will push on their technology -advisory. With the proper preparation, they are able to set up an all-inclusive advice platform that is automated.

Advantages of an automated platform that is advisory

Companies which make an investment in applications and the hardware that enable an automated platform to run with omni-station advice stand to reap tremendous gains. Possibly the most noticeable is client satisfaction; customers who are receiving advice through their favorite channel and at their favorite frequency are substantially more likely to be met with the service they’re receiving and, therefore, are more likely to remain with advisors and the company. An automated advisory platform’s first job will be to identify customer needs and vector those customers into the advice delivery channel that is proper. But, that can not go considerably deeper than the platform. advisors can also use data mining to help them comprehend a customer’s obligations and total assets, much more precisely than before. The sophisticated platforms with artificial intelligence can examine behavioral profile is ’sed by a customer and call possible attrition and life events, then propose methods for the advisors to manage such occasions. The platform may have the capacity to identify possible areas for new product development centered on tasks and customer needs. The real innovators are the ones that will push on their technology -advisory. With the proper preparation, they are able to set up an all-inclusive advice platform that is automated.

Raising price efficiencies

Advisory platforms that are automated enable advisers and companies to be cost efficient in performance and their advice delivery, helping keep gains if fee income declines.

There are, needless to say, upfront prices to add applications and hardware, but automated platforms that are advisory enable advisers and companies to be more cost efficient in performance and their advice delivery, helping keep gains if fee income declines. These platforms also enable adviser and the company serve more customers of every size and kind and to scale up operations.
Past robo-advisory, technology may be a tremendous support in regards to routine account tasks, many of which are never seen by customers.
Likewise, regular coverage—quarterly, annual or as frequently as the customer needs—can be readily automated too.
Eventually, aside from the station, each interaction will be recorded by the finest automated platforms, together with any customer comments, both for to improve future interactions and regulatory functions.
Best interests: The Department of Labor’s fiduciary rule Regulations that are new are about to get stricter, especially in regards to advice on other retirement accounts and IRAs. Additionally, it sets strict rules on advisers becoming paid through commission.
With present adviser practices, the new rule WOn’t probably allow it to be rewarding for advisers to service accounts that are smaller. Nevertheless, technology— especially applications leveraging robo-adviser strategies—can empower companies to service these accounts that are smaller while still fulfilling with the fiduciary rule’s no- best-interest and commission mandates.
The fiduciary rule is more extensive than that, nevertheless, and technology will be critical to fulfilling with all its precepts. Any advice that’s given to some retirement customer must take the customer’s best interest, irrespective of how little or large the customer and no matter what station that advice is delivered through.
The appropriate technology can ensure that occurs, ensuring an adviser or an algorithm doesn’t advocate the investment merchandise that is incorrect, for instance, and making certain the advice is consistent across all stations. Technology also will keep an eye on what advice is given it is simple to demonstrate to regulators that the performance of that advice and the advice were in the customer’s best interest.
Advisory platforms that are automated enable advisers and companies to be cost efficient in performance and their advice delivery, helping keep gains if fee income declines.

The best automated advisory platform will do much more than simply offer investment merchandise suggestions and rebalancing and tax- lost harvest

While the notion of a robo-adviser is not useless, the current use of the technology does not go far enough. This creates an opportunity for forward-thinking financial institutions and advisers reap benefits and to expand the notion significantly, including cementing long-lasting and rewarding client relationships.

The greatest automated advisory platform will do much more than just offer rebalancing and investment merchandise ideas and tax-loss harvesting. The platform also must be incorporated with client relationship management (CRM) programs, so that the adviser can get up to the minute info on an account, regardless of that customer’s main advice delivery channel.

Many of these items can be handled digitally. Others will need call center advice as well as face-to-face meetings between customer and the advisor. Pivoting between channels completely understands each client’s settings and WOn’t be an issue for the company or advisors that has put in place the technology that is correct.

The finest automated advisory platform will do far more than just offer investment product suggestions and rebalancing and tax-lost harvest.

Six steps for delivering automated advisory To construct and produce this vision of a robust, technologyenabled platform, it’s essential the platform be thought out before any changes are made. We have identified six important steps that any automated advisory platform must support while particular functions will differ depending on a firm’s own settings, capabilities, client roster and plans for growth.

Step one – Client vectoring: This critical first step involves getting to know new clients, their assets and their investment aims, and then slotting them into the proper investment and servicing software. Clients can be sifted into groups for example mass market, mass affluent and high net worth, and the platform can devise investment plans for each class (e.g., UMAs for the rich and wrapping accounts for mass market investors) and based on how each client will probably prefer to be served. This assessment must be nimble enough to account for personal tastes.

Measure two – Preparation and product selection: This is the phase that perhaps most resembles now’s robo-adviser. Using information collected through questionnaires and other customer interactions, the algorithm advocates underlying securities which might be suitable to each customer and an asset allocation. The difference is the platform can also help train the client on these options—via online or call center, for example—and may also make adjustments to the recommendations based on how complicated each client’s portfolio and individual financial situation is.

Step three – Implementation: The application or the financial advisor subsequently clarifies the rationale behind the proposed investment program and makes any corrections predicated on client interaction.

Step four – Monitoring: After the automated advisory platform makes an investment, the algorithms continuously track how that investment is performing. The action could be as easy as monitoring rebalancing and drift as needed. The activity also could be more sophisticated, for example tax-loss harvesting, transaction cost optimization, or flagging an issue for additional actions by adviser or the call center.

Step five – Performance and servicing: Here we see the complete advantages of the omni-channel servicing version that an automated platform that is advisory supports. How account statements, investment reports, funding requests and other regular customer messages are best delivered depends on customer setting and adviser commitments, also as on the content of the communications themselves.

Measure six – Event-driven reinvention: Client scenarios continuously change (e.g., new job, inheritance, health complications, retirement). These life events may necessitate significant changes. Many will demand the intervention of a human adviser, although a few of these changes can be managed automatically by the platform. The platform and advisors, working in tandem, can come up with the best solution, such as a brand new investment product, changing to a new service station, or going into an UMA or UMH.

Concerns when choosing provider and a platform

Transforming a current IT system can be expensive, complex and uncertain, but this is offset by the many clear advantages of transitioning to an automated advisory strategy. Questions to consider when looking to purchase and deploy an automated advice platform comprise the following:

• What attributes does the platform The platform also needs to work within a business’s present IT infrastructure, or the business will have to upgrade its systems. Likewise, can the new platform integrate readily with a firm’s present software and with the workflow that’s already in place among its advisers, call center representatives and other professionals?

Scalable have to be addressed? Are there gaps in a firm’s existing data collection systems? Is the company’s online security robust and up up to now? The type of upgrades or changes will be needed to advisers ’ backgrounds and dashboards? What about the call center?

• What are the integration challenges? offer? As we’ve seen, there is certainly a broad range of attributes that are possible a platform will offer. Companies will want to decide how complicated and thorough they desire their first platform to be. In addition they will want to make certain it can be expanded to feature new products and services without an excessive amount of trouble as the business’s needs change.

• What other challenges is the platform? As the company’s novel of business grows firms must ensure the platform can grow with regards to the variety of advisors and clients it can support.


It must not be a question of “if” but instead “when” a company will set up an automated advisory platform. The future of the financial advisory industry will tend heavily on technology, and those companies that lag behind are bound to miss out on the advantages to advisors, customers and the firm overall.

Top companies will put in place platforms that consider the needs of advisors and clients, ensuring that advisers develop a strong instrument that helps them serve their clients more economically and effectively. The robo-adviser tendencies in recent years gave rise to fears that technology would casts aside advisors. But, it is now clear that advisers are as important as ever. An automated advisory platform can augment advisers’ expertise, ensuring that customers receive the best advice—no matter what channel they prefer.

Apache Spark Software Development FinTech

Apache Spark – fast big data processing

Businesses are using Hadoop widely to analyze their data sets. The reason is that Hadoop framework is founded on a straightforward programming model (MapReduce) and it enables a computing solution that is scalable, flexible, fault-tolerant and economical. Here, the main issue would be to keep speed in waiting time to run the program and processing large datasets in terms of waiting time between queries.
Apache Software Foundation for speeding up the Hadoop computational computing software process introduced Spark.
As against a standard idea, Spark is not a modified version of Hadoop and is not, really, dependent on Hadoop because it’s its own cluster direction. Hadoop is only one of the methods to implement Spark.
Spark uses Hadoop in two ways – one is second and storage . It uses Hadoop for storage function only since Spark has its own cluster direction computation.

Apache Spark

Apache Spark is a lightning-quick cluster computing technology, designed for fast computation. It’s based on Hadoop MapReduce and it expands it to be economically used by the MapReduce version for more types of computations, including interactive queries and stream processing. The principal feature of Spark is its in-memory cluster computing that increases the processing speed of an application.


Spark was created to cover a wide variety of workloads such as streaming, iterative algorithms, interactive queries and batch applications. It reduces the management burden of maintaining different tools, besides supporting all these workload in a specific system.

Evolution of Apache Spark

Spark is one of Hadoop’s sub project developed in 2009 in UC Berkeley’s AMPLab by Matei Zaharia. It was Open Sourced in 2010 under a BSD license. It was given to Apache software foundation in 2013, and Apache Spark has become a top level Apache project from Feb-2014.


Characteristics of Apache Spark

Apache Spark has following attributes.


Speed − Spark helps to run an application in Hadoop cluster, 10 times faster when running on disc, and up to 100 times faster in memory. This is possible by reducing amount of read/write operations to disk. It stores the intermediate processing data in memory.


Supports multiple languages − Spark provides built in APIs in Java, Scala, or Python. Consequently can write applications in distinct languages. Spark comes up with 80 high level operators for interactive querying.


Advanced Analytics − Spark supports ‘Map’ and ‘ reduce’. Additionally, it supports SQL queries, Streaming information, Machine learning (ML), and Graph algorithms.

Spark Assembled on Hadoop

The following diagram shows three ways of how Spark can be assembled with Hadoop parts.


Ignite Constructed on Hadoop

There are three manners of Spark installation as described below.


Standalone − Spark Standalone deployment means Spark inhabits the place on top of HDFS(Hadoop Distributed File System) and space is allocated for HDFS, explicitly. Here, MapReduce and Spark will run side by side to cover all spark occupations on bunch.


Hadoop Yarn − Hadoop Yarn deployment means, only, spark runs with no pre-installation or root access needed on Yarn. It helps to integrate Spark into Hadoop stack or Hadoop ecosystem. It enables other components to run in addition to stack.


Spark in MapReduce (SIMR) − Spark in MapReduce is used to launch spark occupation in addition to standalone deployment. With SIMR, Spark can be started by user and uses its shell with no administrative access.

Parts of Spark

Apache Spark Software Development FinTech

Apache Spark Core

Spark Core is the inherent general execution engine for Spark platform that all other functionality is built upon. It provides In-Memory computing and referencing datasets in external storage systems.


Spark SQL

Spark SQL is a part in addition to Spark Core that introduces a new data abstraction called SchemaRDD, which provides support for structured and semi-structured data.


Start Streaming

Spark Streaming leverages Spark Core’s fast scheduling capability to perform streaming analytics. It ingests info in mini-batches and performs RDD (Bouncy Distributed Datasets) transformations on those mini-batches of data.


MLlib (Machine Learning Library)

MLlib is a distributed machine learning framework above Spark due to the distributed memory-based Spark design. It is, according to benchmarks, done by the MLlib developers against the Alternating Least Squares (ALS) enactments. Spark MLlib is nine times as rapid as the Hadoop disk-based version of Apache Mahout (before Mahout gained a Spark interface).



GraphX is a distributed graph-processing framework in addition to Spark. It supplies an API for expressing graph computation that can model the user- . Additionally, it provides an optimized runtime for this abstraction.


Spark vs Hadoop


Listen in on any conversation about big data, and you’ll probably hear mention of Hadoop or Apache Spark. Here is a brief look at what they do and how they compare.


1. They do different things. Hadoop and Apache Spark are both big-data frameworks, but they don’t actually serve the same functions. Hadoop is basically a distributed information infrastructure: It doles out huge data collections across multiple nodes within a cluster of commodity servers, which means you do not need to buy and keep expensive custom hardware. In addition, it indexes and keeps track of that info, empowering big-data analytics and processing far more effectively than was possible previously. Spark, on the other hand, is a data-processing tool that operates on those distributed data collections; it doesn’t do distributed storage.


2.  You can use one without the other. Hadoop includes not only a storage part, referred to as the Hadoop Distributed File System, so you don’t need Spark to get your processing, but also a processing part called MapReduce. Conversely, you can even use Spark without Hadoop. Spark does not come with its own file management system, though, so it must be integrated with one — if not HDFS, afterward another cloud-based info platform. Spark was designed for Hadoop, however, so many agree they’re better collectively.


3. Spark is quicker. Spark is usually a lot quicker than MapReduce because of the way it processes data. Spark functions on the entire data set in one fell swoop while MapReduce operates in measures. “The MapReduce workflow looks like this: read information from the cluster, perform an operation, write results to the cluster, read updated information from the cluster, perform next operation, write next results to the bunch, etc.,” explained Kirk Borne, principal info scientist at Booz Allen Hamilton. Spark, on the other hand, finishes the full data analytics operations in-memory and in near real time: “Read information from the bunch, perform all the necessary analytic operations, write results to the cluster, done,” Borne said. Spark can be as much as 10 times quicker than MapReduce for batch processing and up to 100 times quicker for in-memory analytics, he said.



4. You may not need Spark’s speed. MapReduce’s processing fashion can be just fine if your data operations and reporting conditions are mostly static and you’ll be able to wait for batch-mode processing. But if you must do analytics on streaming data, like from detectors on a factory floor, or have applications that need multiple operations, you probably want to go with Spark. Most machine learning algorithms, by way of example, need multiple procedures. Common uses for Spark contain real-time marketing campaigns, product recommendations that are online, cybersecurity analytics and machine log monitoring.

Financial Technology Software Development

Best software development practices

Most software projects fail. In reality, the Standish group reports that over 80% of jobs are unsuccessful because they’re over budget, late, missing function, or a blend. Also, 30% of software projects are so poorly executed that they are canceled before completion. In our expertise, applications projects using modern technologies like Java, J2EE, XML, and Web Services are no exception to this rule.
This article includes a summary of best practices for software development jobs. Business luminaries such as Scott Ambler, Martin Fowler, Steve McConnell, and Karl Wiegers have recorded many of these best practices online and they are referenced in this article. See the Related information section at the end of this article. The companion article, Guide to Running Software Development Projects, describes the top ten variables that help enhance the success of your project.
Best software development practices
1. Development procedure – It’s important because all other actions are derived from the procedure to pick the development lifecycle process that is proper to the software project at hand. Over a waterfall process, some kind of spiral-based methodology is used for most modern software development jobs. Having a procedure is better than not having one at all, and in many cases it’s less important than how well it is implemented on what procedure is used,. The generally used methodologies listed above all feature guidance about the best way to implement the procedure and templates for artifacts.
2. Requirements – Gathering and concurring on requirements is essential to a successful software project. This does not automatically entail that all requirements have to be fixed before any architecture, design, and coding but it really is essential for the software development team to realize what has to be built. Quality requirements are broken up into two kinds: functional and nonfunctional. A good means to record functional requirements is using Use Cases. An authoritative publication on the matter of use cases is by Armour and Miller [5]. Non-functional requirements describe the functionality and system features of the program. It’s important to collect them because they have a major impact on the software architecture, design, and functionality.
3. Architecture – Picking the appropriate design for your software  is crucial. Many times we’ve found that the software development team didn’t implement well-known architecture best practices.Practices that are tried and true are called patterns and they range from the classic Gang of Four [6] patterns, Java designs [7], to EJB design patterns [8]. The equivalent of Sun is the Core J2EE Patterns catalogue [9]. Many software projects fail as discussed in the introduction. The study of these failures has given rise to the notion of antipatterns. They’re valuable because they provide useful knowledge of what doesn’t work, and why.
4. Design – Even with a superb software architecture it really is still possible to have a lousy layout. Many programs are over-designed or under-designed. Reuse is one of the great promises of OO, but it’s often unrealized because of the added effort needed to create reusable assets. Code reuse is but one form of reuse and there are other kinds of reuse that can supply better productivity increases.
Agile teams are under pressure to deliver working. They’re additionally available to their customers for possibly radical requirements changes at any stage in the endeavor. They and their code must be capable of turning on a dime at any moment. So agile teams put tremendous value on the extensibility of their code: the extent to which they can easily maintain and expand it. Elsewhere we discuss how refactoring that is important is to keeping code extensible. The other vital component of extensibility is code design ease. Extensibility seems to be inversely proportional to design complexity.
In any agile context, straightforward design means, to paraphrase the poet Wallace Stevens, “the art of what suffices.” It means programming for today’s prerequisites that are stated, and more. It means doing more with less. But this isn’t always a natural disposition for us programmers.
But the truth about layout intricacy of all kinds is that we often find that technologies or the additional abstractions don’t become wings that free us, but rather shackles that bind us. Whatever additional stuff we add, we’re indeed clamping to our legs, to lug around then from feature to feature, from iteration to iteration, and from release to release. There are mountains of distressing old lessons behind this maxim.
5. Construction of the code – Construction of the code is a fraction of the overall software project effort, but it’s often the most observable. Requirements, architecture, analysis, design, and test are included by other work important. In endeavors with no software development process (so-called “code and fix”), these jobs are also occurring, but under the guise of programming. A best practice for building code comprises the daily build and smoke test. Martin Fowler goes one step further and suggests continuous integration that also integrates the concept of self and unit tests -testing code. Note that even though continuous integration and unit tests have gained popularity through XP, you can use these best practices on all types of endeavors. I advocate using frameworks that are standard to automate builds and testing, such as JUnit and Ant.
Which is to say, it is easier for them to keep the flaws in the code to really low levels, and hence more easy for them to add features, make changes, and still produce very low-defect code every iteration.
After experimenting with different ways to keep upward evaluation coverage at those optimum amounts, agile teams hit upon the practice of Test-First programming. Test-First programming demands creating automated unit tests for production code, before that production code is written by you. Instead of writing evaluations afterward (or, more usually, not ever writing those evaluations), you always start with an unit test. This evaluation might not even compile, in the beginning, because not all the classes and methods it requires may exist. Nevertheless, it functions as a kind of executable specification. (Occasionally you expect it to fail, and it passes, which is beneficial info.) You subsequently make just as much code as will empower that test to pass.
6. Pair Programming – It is vital that you review other people’s work. Experience has provided evidence for that difficulties are removed before this way and reviews are powerful or more effective than testing. Any artifact from the software development procedure is reviewed, including test cases, and strategies, requirements, structure, design, code. Peer reviews are helpful in trying to create software quality at top speed.
Research results and anecdotal reports seem to show that short-term productivity might fall modestly (about 15%), but because the code generated is so much better, long-term productivity goes up. And definitely it is dependent upon how you measure productivity, and over what period. In an agile context, productivity is frequently quantified in running, tested features truly delivered per iteration and per release.
Definitely as a mechanism that is mentoring, pairing is tough to beat. If pairs switch off consistently (as they should), pairing disperses knowledge throughout the team with great efficacy.
7. Testing – Testing is not cutback or an afterthought when the program gets tight. It’s also important that testing is done proactively; test cases are developed while the program is being designed and coded, and significance that test cases are planned before coding starts. Additionally, there are several testing routines which were developed.
8. Performance testing – Testing is generally the last resort to catch program flaws. It generally just catches coding flaws and is labor intensive. Design and architecture flaws may be missed. One method to get some architectural defects is to mimic load testing on the program before it is deployed and to take care of performance issues before they become problems.
9. Continuous Integration – Traditional software development approaches do’t order how frequently or regularly you integrate all the source on a software project. Programmers can work individually for hours, days, or even weeks on the same source without recognizing how many conflicts (and maybe bugs) they are creating. Agile teams, because they are producing robust code each iteration, generally find they are slowed down by the long diff- resolution and debugging sessions that frequently occur at the ending of long integration cycles. The more the code is being shared by programmers, the more problematic this is. For these reasons, agile teams frequently consequently choose to use Continuous Integration.
Agile teams generally configure CI to contain source control integration, unit test execution, and automated compilation. Sometimes CI also comprises automatically running automated approval tests for example those developed using FitNesse.
10. Quality and defects direction – It’s crucial that you establish launch standards and quality priorities for the software project so that a strategy is built to help the team achieve quality applications. As the job is coded and analyzed, fix speed and the defect coming can help measure the maturity of the code. It is important that a defect tracking system is used that is linked to the source control management system. For example, jobs using Rational ClearCase may also use Rational ClearQuest. By using defect tracking, it is not impossible to gauge when a software project is prepared to release.
11. Code Refactoring – refactoring is the process of simplifying and clarifying the layout of existing code, without changing its behavior. This is because un-refactored code has a tendency to rot. Rot takes several kinds: unhealthy addictions between classes or packages, lousy allotment of class duties, way too many duties per method or group, duplicate code, and many other varieties of confusion and clutter.
Every time code changes without refactoring it, rot worsens and spreads. Us frustrates, costs us time, and unduly shortens the lifespan of useful systems.
Refactoring code ruthlessly prevents rot, keeping the code simple to maintain and expand. This is the measure of its success and the reason to refactor. But notice that it is only “ safe” to refactor the code this widely if we’ve got wide-ranging unit test suites of the type we get if we work Evaluation-First. We run the risk of introducing bugs, without being able to run those tests after each little step in a refactoring. If you’re doing true Test-Driven Development (TDD), in which the design evolves constantly, then you’ve got no choice about regular refactoring, since that’s how you develop the layout.
12. Deployment – Deployment is the final stage of releasing an application for users. If you get this far in your software project – congratulations! However, there are still things that can FAIL. You should plan for deployment and a deployment checklist can be used by you on the Construx Web site.
13. System operations and support – Without the operations section, you cannot deploy and support a brand new program. The support area is an essential component to react and resolve user problems. To ease the flow of difficulties, the support issue database is hooked into the program defect tracking system.
14. Data migration – Most applications aren’t brand new, but are rewrites or improvements of existing applications. Data migration from the present data sources is typically a significant endeavor by itself. This isn’t a software project for your junior programmers. It is as important as the new program. Generally the new application anticipates higher quality data and has business rules that are better. Improving the quality of data is a complex matter outside the scope of this article.
15. Project management – Many of the other best practice places described in this post are related to project management and an excellent project manager is conscious of the existence of these best practices. Our recommended bible for project management is Rapid Development by Steve McConnell [14]. One method to manage a job that is difficult is through timeboxing.