Software quality management system

These costs could include:. These systems are a lifelong commitment. So ask yourself, how much is your company willing to spend on Quality Management Systems. The primary reason for an organization to seek the implementation of new software to manage their quality-related documentation is to improve efficiency. Supporting and expanding company objectives can be achieved through the competent use of these systems. Organizations can also gain improved productivity and achievement of long-term goals through an efficient QMS.

To be able to effectively make use of a Quality Management System, the head of the organization needs to make sure the employees are willing to spend as much time required to get the maximum use of these systems. Additionally, the company should ask themselves if there is going to be sufficient time to put aside for the management and maintenance of the new system.

And if so, would that time be well spent? In fact, when considering the right QMS, it is essential to make sure the qualities of the particular QMS in mind would comply with the requirements of the organization. This would, in turn, guarantee efficiency. For an organization to move forward in the current competitive market, Quality Management Software is a great support.

These systems would guarantee that any company would receive the boost it requires. Through that, organizations can then ensure maximum efficiency, increased productivity, and higher profits. If a Quality Management Software System is supposedly in the best interest of a company, they should make it a vital point to determine the different kinds of metrics or strategies to be used as a baseline for improvement.

The following methods can be used as a baseline for accelerating a Return on Investment for Quality Management Software:. With all the information provided above, companies can create a better organizational vision in the long run.

However, it is essential to keep in mind that this vision would require constant updating for it to be effective. The same approach can be directed to ensure organizational efficiency with Quality Management System Software. Harrington Group International HGI is an organization that issues some of the best software solutions to companies worldwide. Through our long-standing reputation for the best software solutions, here at HGI, we only guarantee the best services for each client.

Our HQMS Software brings potential clients with a state of the art solution for quality management plans. Our software guarantees that clients would be provided with quality monitoring and assurance to ensure that they get their products ready and compliant for business processing. Here at Harrington Group International, client requirements are the priority, and we ensure that each HQMS Software provided would be catered according to the needs of clients while offering some of the most beneficial services.

These services include:. We bring knowledge and in-depth functional expertise while guaranteeing a practical approach to build capabilities to deliver an authentic impact to ensure organizational efficiency.

Harrington Quality Management System HQMS is our flagship world-class software for enterprise process improvement, compliance and qual. The caWeb Issue Action Software Solution gives your organization easy steps to identify any issue, designate the teams members and execu.

We develop cost effective business management tools that will evolve with your business as it grows…. Home Products Quality Management Systems. Using the ISO standard for organizations would help them: Organize their processes for efficiency. Ensure there is an improvement in the effectiveness of their operations.

Make sure there is continuous improvement in their business processes. These properties include: Management of Data. Internal processing of organizational information. Customer satisfaction through high-quality products. Identifying areas for improvements. Quality Analysis. Does your company require a Quality Management Software or qms software systems?

Implementation of a QMS into a company that does not require one could result in: Loss of productivity. Loss of profits. Difficulty locating documents. Possible reasons for QMS System implementation failure: It is often found that in many cases, organizations that have QMS implementation failures often blame the software. A few of which could include: The basis of implementation. Software Customization. Inadequate user training. Poor testing of software.

Through the utilization of a Quality Management Software or qms software systems in the right way, it could be significantly beneficial to a company offering the following advantages: Improvement of processes. Significant waste reduction. Less unnecessary company spending. Opportunities to improve and document training processes.

Identifying broad business objectives. To ensure that an effective strategy for Quality is pursued, the following factors should be what your Quality Management Software delivers: Accurate information.

Easy to process and understand. Easy online access. On-time accessibility. Luckily, you can use quality management system software to help you achieve your management goals. Continue reading below to learn more and see our software recommendations. Greenlight Guru Quality Management Software 2.

Qualio 4. EASE 6. Intellect 7. TrackWise 9. ACE IAuditor IFS Measurement is a direct quantification whereas calculation is an indirect one where we combine different measurements using some formulae.

Software Engineering involves managing, costing, planning, modeling, analyzing, specifying, designing, implementing, testing, and maintaining software products. Hence, measurement plays a significant role in software engineering.

A rigorous approach will be necessary for measuring the attributes of a software product. Thus, for controlling software products, measuring the attributes is necessary. Every measurement action must be motivated by a particular goal or need that is clearly defined and easily understandable. The measurement objectives must be specific, tried to what managers, developers and users need to know. Measurement is required to assess the status of the project, product, processes, and resources.

Measurement tells us the rules laying the ground work for developing and reasoning about all kinds of measurement. It is the mapping from the empirical world to the formal relational world.

Consequently, a measure is the number or symbol assigned to an entity by this mapping in order to characterize an entity. Empirical relations in the real world can be mapped to a formal mathematical world.

Mostly these relations reflect the personal preferences. Very superior About the same Very inferior. To perform the mapping, we have to specify domain, range as well as the rules to perform the mapping. Similarly, in case of software measurement, the checklist of the statement to be included in the lines of code to be specified. The representational condition asserts that a measurement mapping M must map entities into numbers, and empirical relations into numerical relations in such a way that the empirical relations preserve and are preserved by numerical relations.

Since, there can be many relations on a given set, the representational condition also has implications for each of these relations. Models are useful for interpreting the behavior of the numerical elements of the real-world entities as well as measuring them. To help the measurement process, the model of the mapping should also be supplemented with a model of the mapping domain. A model should also specify how these entities are related to the attributes and how the characteristics relate.

These are the measurements that can be measured without the involvement of any other entity or attribute. For allocating the appropriate resources to the project, we need to predict the effort, time, and cost for developing the project. The measurement for prediction always requires a mathematical model that relates the attributes to be predicted to some other attribute that we can measure now. Hence, a prediction system consists of a mathematical model together with a set of prediction procedures for determining the unknown parameters and interpreting the results.

Measurement scales are the mappings used for representing the empirical relation system. It places the elements in a classification scheme. The classes will not be ordered. Each and every entity should be placed in a particular class or category based on the value of the attribute. The empirical relation system consists only of different classes; there is no notion of ordering among the classes.

Any distinct numbering or symbolic representation of the classes is an acceptable measure, but there is no notion of magnitude associated with the numbers or symbols. It places the elements in an ordered classification scheme. The empirical relation system consists of classes that are ordered with respect to the attribute.

The numbers represent ranking only. Hence, addition, subtraction, and other arithmetic operations have no meaning. This scale captures the information about the size of the intervals that separate the classification. Hence, it is more powerful than the nominal scale and the ordinal scale.

This is the most useful scale of measurement. Here, an empirical relation exists to capture ratios. It is a measurement mapping that preserves ordering, the size of intervals between the entities and the ratio between the entities. On this scale, there will be only one possible measure for an attribute.

Hence, the only possible transformation will be the identity transformation. Empirical Investigations involve the scientific investigation of any tool, technique, or method. This investigation mainly contains the following 4 principles. Survey is the retrospective study of a situation to document relationships and outcomes.

It is always done after an event has occurred. For example, in software engineering, polls can be performed to determine how the users reacted to a particular method, tool, or technique to determine trends or relationships.

In this case, we have no control over the situation at hand. We can record a situation and compare it with a similar one. It is a research technique where you identify the key factors that may affect the outcome of an activity and then document the activity: its inputs, constraints, resources, and outputs. It is a rigorous controlled investigation of an activity, where the key factors are identified and manipulated to document their effects on the outcome.

If the activity has already occurred, we can perform survey or case study. If it is yet to occur, then case study or formal experiment may be chosen. If we have a high level of control over the variables that can affect the outcome, then we can use an experiment. If we have no control over the variable, then case study will be a preferred technique. To boost the decision of a particular investigation technique, the goal of the research should be expressed as a hypothesis we want to test.

The hypothesis is the tentative theory or supposition that the programmer thinks explains the behavior they want to explore. After stating the hypothesis, next we have to decide the different variables that affect its truth as well as how much control we have over it.

This is essential because the key discriminator between the experiment and the case studies is the degree of control over the variable that affects the behavior. A state variable which is the factor that can characterize the project and can also influence the evaluation results is used to distinguish the control situation from the experimental one in the formal experiment. If we cannot differentiate control from experiment, case study technique will be a preferred one.

For example, if we want to determine whether a change in the programming language can affect the productivity of the project, then the language will be a state variable. The results of an experiment are usually more generalizable than case study or survey. The results of the case study or survey can normally be applicable only to a particular organization. Following points prove the efficiency of these techniques to answer a variety of questions. Case studies or surveys can be used to conform the effectiveness and utility of the conventional wisdom and many other standards, methods, or tools in a single organization.

However, formal experiment can investigate the situations in which the claims are generally true. The relationship among various attributes of resources and software products can be suggested by a case study or survey. For example, a survey of completed projects can reveal that a software written in a particular language has fewer faults than a software written in other languages. Understanding and verifying these relationships is essential to the success of any future projects.

Each of these relationships can be expressed as a hypothesis and a formal experiment can be designed to test the degree to which the relationships hold. Usually, the value of one particular attribute is observed by keeping other attributes constant or under control. Models are usually used to predict the outcome of an activity or to guide the use of a method or tool. It presents a particularly difficult problem when designing an experiment or case study, because their predictions often affect the outcome.

The project managers often turn the predictions into targets for completion. This effect is common when the cost and schedule models are used. Some models such as reliability models do not influence the outcome, since reliability measured as mean time to failure cannot be evaluated until the software is ready for use in the field.

There are many software measures to capture the value of an attribute. Hence, a study must be conducted to test whether a given measure reflects the changes in the attribute it is supposed to capture. Validation is performed by correlating one measure with another. A second measure which is also a direct and valid measure of the affecting factor should be used to validate.

Such measures are not always available or easy to measure. Also, the measures used must conform to human notions of the factor being measured. Internal attributes are those that can be measured purely in terms of the process, product, or resources itself. For example: Size, complexity, dependency among modules. External attributes are those that can be measured only with respect to its relation with the environment.

For example: The total number of failures experienced by a user, the length of time it takes to search the database and retrieve information. Processes are collections of software-related activities. The different external attributes of a process are cost, controllability, effectiveness, quality and stability. Products are not only the items that the management is committed to deliver but also any artifact or document produced during the software life cycle.

The different internal product attributes are size, effort, cost, specification, length, functionality, modularity, reuse, redundancy, and syntactic correctness. Among these size, effort, and cost are relatively easy to measure than the others. The different external product attributes are usability, integrity, efficiency, testability, reusability, portability, and interoperability.

These attributes describe not only the code but also the other documents that support the development effort. These are entities required by a process activity. It can be any input for the software production. It includes personnel, materials, tools and methods.

The different internal attributes for the resources are age, price, size, speed, memory size, temperature, etc. The different external attributes are productivity, experience, quality, usability, reliability, comfort etc. A particular measurement will be useful only if it helps to understand the process or one of its resultant products.

The improvement in the process or products can be performed only when the project has clearly defined goals for processes and products. A clear understanding of goals can be used to generate suggested metrics for a given project in the context of a process maturity framework.

Deriving the questions from each goal that must be answered to determine if the goals are being met. To use GQM paradigm, first we express the overall goals of the organization.

Then, we generate the questions such that the answers are known so that we can determine whether the goals are being met. Later, analyze each question in terms of what measurement we need in order to answer each question. Typical goals are expressed in terms of productivity, quality, risk, customer satisfaction, etc.

Goals and questions are to be constructed in terms of their audience. Example : To characterize the product in order to learn it. Example : Examine the defects from the viewpoint of the customer. Example : The customers of this software are those who have no knowledge about the tools. According to the maturity level of the process given by SEI, the type of measurement and the measurement program will be different.

Following are the different measurement programs that can be applied at each of the maturity level. At this level, the inputs are ill- defined, while the outputs are expected.

The transition from input to output is undefined and uncontrolled. For this level of process maturity, baseline measurements are needed to provide a starting point for measuring. At this level, the inputs and outputs of the process, constraints, and resources are identifiable. A repeatable process can be described by the following diagram. The input measures can be the size and volatility of the requirements.

The output may be measured in terms of system size, the resources in terms of staff effort, and the constraints in terms of cost and schedule. At this level, intermediate activities are defined, and their inputs and outputs are known and understood. A simple example of the defined process is described in the following figure. The input to and the output from the intermediate activities can be examined, measured, and assessed. At this level, the feedback from the early project activities can be used to set priorities for the current activities and later for the project activities.

We can measure the effectiveness of the process activities. The measurement reflects the characteristics of the overall process and of the interaction among and across major activities.

At this level, the measures from activities are used to improve the process by removing and adding process activities and changing the process structure dynamically in response to measurement feedback. Thus, the process change can affect the organization and the project as well as the process. The process will act as sensors and monitors, and we can change the process significantly in response to warning signs. At a given maturity level, we can collect the measurements for that level and all levels below it.

Process maturity suggests to measure only what is visible. Thus, the combination of process maturity with GQM will provide most useful measures. At level 1 , the project is likely to have ill-defined requirements.

At this level, the measurement of requirement characteristics is difficult. At level 2 , the requirements are well-defined and the additional information such as the type of each requirement and the number of changes to each type can be collected. At level 3 , intermediate activities are defined with entry and exit criteria for each activity.

The goal and question analysis will be the same, but the metric will vary with maturity. The more mature the process, the richer will be the measurements. The GQM paradigm, in concert with the process maturity, has been used as the basis for several tools that assist managers in designing measurement programs. GQM helps to understand the need for measuring the attribute, and process maturity suggests whether we are capable of measuring it in a meaningful way.

Together they provide a context for measurement. Measures or measurement systems are used to asses an existing entity by numerically characterizing one or more of its attributes.

A measure is valid if it accurately characterizes the attribute it claims to measure. Validating a software measurement system is the process of ensuring that the measure is a proper numerical characterization of the claimed attribute by showing that the representation condition is satisfied. For validating a measurement system, we need both a formal model that describes entities and a numerical mapping that preserves the attribute that we are measuring.

For example, if there are two programs P1 and P2, and we want to concatenate those programs, then we expect that any measure m of length to satisfy that,. If a program P1 has more length than program P2 , then any measure m should also satisfy,. The length of the program can be measured by counting the lines of code. If this count satisfies the above relationships, we can say that the lines of code are a valid measure of the length. The formal requirement for validating a measure involves demonstrating that it characterizes the stated attribute in the sense of measurement theory.

Prediction systems are used to predict some attribute of a future entity involving a mathematical model with associated prediction procedures. Validating prediction systems in a given environment is the process of establishing the accuracy of the prediction system by empirical means, i.

It involves experimentation and hypothesis testing. The degree of accuracy acceptable for validation depends upon whether the prediction system is deterministic or stochastic as well as the person doing the assessment. Some stochastic prediction systems are more stochastic than others. Examples of stochastic prediction systems are systems such as software cost estimation, effort estimation, schedule estimation, etc.

Hence, to validate a prediction system formally, we must decide how stochastic it is, then compare the performance of the prediction system with known data. Software metrics is a standard of measure that contains many activities which involve some degree of measurement.

It can be classified into three categories: product metrics, process metrics, and project metrics. Product metrics describe the characteristics of the product such as size, complexity, design features, performance, and quality level.

Process metrics can be used to improve software development and maintenance. Examples include the effectiveness of defect removal during development, the pattern of testing defect arrival, and the response time of the fix process. Project metrics describe the project characteristics and execution. Software measurement is a diverse collection of these activities that range from models predicting software project costs at a specific stage to measures of program structure. Effort is expressed as a function of one or more variables such as the size of the program, the capability of the developers and the level of reuse.

Cost and effort estimation models have been proposed to predict the project cost during early phases in the software life cycle. Productivity can be considered as a function of the value and the cost. Each can be decomposed into different measurable size, functionality, time, money, etc. Different possible components of a productivity model can be expressed in the following diagram.

The quality of any measurement program is clearly dependent on careful data collection. Data collected can be distilled into simple charts and graphs so that the managers can understand the progress and problem of the development. Data collection is also essential for scientific investigation of relationships and trends. Quality models have been developed for the measurement of quality of the product without which productivity is meaningless.

These quality models can be combined with productivity model for measuring the correct productivity. These models are usually constructed in a tree-like fashion. The upper branches hold important high level quality factors such as reliability and usability.

The notion of divide and conquer approach has been implemented as a standard approach to measuring software quality. Most quality models include reliability as a component factor, however, the need to predict and measure reliability has led to a separate specialization in reliability modeling and prediction.

The basic problem in reliability theory is to predict when a system will eventually fail. It includes externally observable system performance characteristics such as response times and completion rates, and the internal working of the system such as the efficiency of algorithms. It is another aspect of quality. Here we measure the structural attributes of representations of the software, which are available in advance of execution.

Then we try to establish empirically predictive theories to support quality assurance, quality control, and quality prediction. This model can assess many different attributes of development including the use of tools, standard practices and more.

It is based on the key practices that every good contractor should be using. For managing the software project, measurement has a vital role. For checking whether the project is on track, users and developers can rely on the measurement-based chart and graph. The standard set of measurements and reporting methods are especially important when the software is embedded in a product where the customers are not usually well-versed in software terminology.

This depends on the experimental design, proper identification of factors likely to affect the outcome and appropriate measurement of factor attributes. Software metrics is a standard of measure that contains many activities, which involves some degree of measurement. The success in the software measurement lies in the quality of the data collected and analyzed. Are they correct?