Metrics and Models in Software Quality Engineering

Article 1: Hariprasad, T., et al. "Software complexity analysis using halstead metrics." 2017 International Conference on Trends in Electronics and Informatics (ICEI). IEEE, 2017.

It is quite evident that the complexity of software impacts inward connection regarding programming, and the complexities are problematic to determine without using appropriate measurements. Cumulatively, exertion, timeframe, and unpredictability vary from one program to another. As a result, this article accentuates on Halstead measurements provided to identify programs’ product complexity by employing a source line by obtaining help from operators and operands. Furthermore, Halstead’s metric is used to decide an effective quantitative complexity measure, specifically from operators and operands. The article provides a connection between programs formulated in dual dialects to determine exertion, time, and unpredictability and examine the best programming dialect that required less execution and minimum exertion. Programming is only accomplished whenever customers’ needs are fully met. This article offers relevant Halstead acquainted measurements that assess the complexity of programming that may be challenging to quantify with insufficient measurements. As such, Halstead metrics measurements evaluate a program by identifying the time needed for testing, available mistakes, exertion quantity, and its multidimensional nature when implementing the same programs in diversified dialects.

Critical Analysis

This article offers in-depth and rich information regarding Halstead metrics in measuring the complexity of programs prior to the execution process. I find this source vital as it discusses several framework interfaces, together with complex certainties that make a software complex, which in some cases gets out of hand, thus stimulating increased hazards and costs during an upgrade. By using Halstead metrics, it is easy to actualize and does not necessitate thorough scrutiny of a program’s structure. Practically, these metrics can enhance quality by identifying the product’s software complexity, hazards that may arise, estimate, exertion, and timeframe. Even when a program is offered in different dialects at different circumstances, these metrics will effectively ascertain precise amounts and offer viable proof to code of lines, together with the program’s extent to which a program can perform with minimal unpredictability. In the future, research should be made on how Halstead metrics can be used to plan ventures that can effectively back up Halstead’s programming bugs.

Article 2: Abd Jader, Marwa Najm, and Riyadh Zaghlool Mahmood. "Calculating Mccabe's Cyclomatic Complexity Metric and Its Effect on The Quality Aspects Of Software." International Journal of Innovative Research and Creative Technology. Vol. 3. No. 5 (March-2018). IJIRCT, 2018.

It is imperative to measure the complexity of software since, currently, scholars have identified that high complexity issues are the main reasons for difficulty in comprehending and analyzing problematic software changes in the near future. Moreover, it has been the primary foundation of poor quality and defects in software today. As such, this article mainly illustrates high complexity issues resulting in severe faults in numerous software, which in turn cause high to maintain and fixing costs. In light of this, software complexity metrics identify software quality improvements, as well as project controllability in general. Using software complexity metric calculations, this article simplifies the testing process and warrants software execution. In other words, the total amount of test cases will equate to a program’s Cyclomatic Complexity, which pinpoints levels of reliability risks, as well as effort/cost, thereby minimizing maintenance risk levels by basically relying on Cyclomatic Complexity Matrics.

Critical Analysis

I find that measuring the complexity of software is an imperative tenet when measuring the quality of the software since reading and comprehending it may be problematic, and I concur with the fact that the primary reason behind it is complex software. Additionally, the article offers insightful research and ideas regarding efforts required to describe and analyze the design, requirements, system debugging, and code that originates under major effects of software complexity when creating the software’s phases. Complexity metrics are relevant to the class and my line of work as they create the best sources of assisting in making intricate decisions regarding planning and strategies of projects. Practically, they can assist in adjusting various programming estimates, as well as duly costs and schedules. Additional research should be based on the development of more suitable and extensive testing plans to lessen high complex code changes.

Article 3: Sarala, S., and P. Abdul Jabbar. "Information flow metrics and complexity measurement." 2010 3rd International Conference on Computer Science and Information Technology. Vol. 2. IEEE, 2010.

This article mainly focuses on complexity metrics, which play a vital role in predicting rates of failure and faulty density in software utilization. It states that the flow of information depicts data flow in cumulative procedures within concrete systems’ processes. Therefore, it analyzes code sources, and an aptitude of the complexity metrics are able to predict the complexity of data flow actual amounts. By so doing, complexity metrics are used to enhance the estimation method of data flow complexity that is majorly employed in assessing static measures of code source and efficiently facilitating restriction of present work. Therefore, complexity metrics are significantly described in that they ensure a combination of data flow with an external active strategy of information flow that relies on source codes to ensure the quality of a software.

Critical Analysis

The intellectual merit of this article is based on the idea behind complexity metrics, which determines software module complexity by recognizing functional design units allied to the quantity of data flows between a system and its location. However, some factors are loosely derived as they lack sufficient back-up from the evaluation criteria used in this article. Therefore, practically, there are various hindrances that complexity metrics may cause, including the absence of cohesion and modules used as stress points, of which modifying such modules may result in broad modification of the entire system, causing the inconsistent quality of programs. Furthermore, since the results are dissimilar, thereby creating mixed outcomes, further research is necessary, in association with other metrics, to showcase the specific software aspects that should be addressed to warrant its quality and that of the data it generates.

Article 4: Quesada-López, Christian, and Marcelo Jenkins. "Function Point Structure and Applicability: A Replicated Study." J. Object Technol. 15.3 (2016): 2-1.

The intricacy of giving an accurate effort prediction model and software size is acknowledged in the software field. This article accentuates functional point analysis as a crucial metric, which is currently the most recognized software metric in the field, even though it is rarely automatable as it needs extensive, costly processes. Additionally, this literature evaluates the applicability and structure of function point metrics to collect evidence regarding the quality of various data sets. Combined with unadjusted function points, this metric was used to assess if basic sizing metrics can suitably predict the quality and accuracy of the software.

Critical Analysis

It is important to note that the process of estimating software is a vital factor in ensuring the quality and success of the software project. Therefore, the functional point analysis model was effectively illustrated to assess the software's complexity and accuracy. Since inaccurate estimates have been noted as outstanding issues that cause missed deadlines and low quality of software programs, the literature extensively discusses functional size estimates as a way of creating quality and financial indicators, which increase productivity in various phases of the process of software creation. Most importantly, this metric offers numerous drivers that estimate the factors that can enhance model accuracy. As such, it is advisable that in the future, data estimations should be included when evaluating software function points to improve the quality of programs.

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Works Cited

Abd Jader, Marwa Najm, and Riyadh Zaghlool Mahmood. "Calculating Mccabe's Cyclomatic Complexity Metric and Its Effect on the Quality Aspects Of Software." International Journal of Innovative Research and Creative Technology. Vol. 3. No. 5 (March-2018). IJIRCT, 2018.

Hariprasad, T., et al. "Software complexity analysis using halstead metrics." 2017 International Conference on Trends in Electronics and Informatics (ICEI). IEEE, 2017.

Quesada-López, Christian, and Marcelo Jenkins. "Function Point Structure and Applicability: A Replicated Study." J. Object Technol. 15.3 (2016): 2-1.

Sarala, S., and P. Abdul Jabbar. "Information flow metrics and complexity measurement." 2010 3rd International Conference on Computer Science and Information Technology. Vol. 2. IEEE, 2010.