Software Engineering Measurement Research Articles

A novel metric for assessing structural complexity of data warehouse requirements models

Metrics can be used to evaluate the overall structural complexity of the data warehouse (DW) and assess its quality in the early phases of design and development. The requirements data model plays a crucial role in developing a quality-based DW. Several researchers have assessed the quality of the requirements data model using the requirements metrics based on the agent-goal-decision-information (AGDI) model, both formally and empirically. However, no assessment has been implemented on the structural complexities of the requirements data model based on goal, decision, and information hierarchies. Considering this, the study proposes a novel structural complexity metric to assess the overall complexity of the requirements data model at different phases (information, decision and organisation) and to analyze the links between the different levels of granularity in these models. The proposed metric is formally validated by applying the measurement theory-based Briand’s framework for complexity measure (i.e., one of the five notions of measurements in Software Engineering) by confirming that the proposed metric is valid and defined correctly under certain mathematical properties. As a result, we conclude that the proposed structural complexity metric is accurately defined and effectively valid. We also provide a detailed comparison of various requirements metrics commonly used in similar contexts to contextualize our proposed metric within the broader landscape of existing methodologies. Additionally, we have included comprehensive guidelines and practical examples for applying the proposed structural complexity metric in DW development scenarios to enhance its utility among practitioners. The proposed metric can be utilized to ensure the accuracy of the requirements model, thereby improving the overall quality of DW.

Measuring and improving software requirements elicitation in a small-sized software organization: a lightweight implementation of ISO/IEC/IEEE 15939:2017—systems and software engineering—measurement process

Measuring and improving software requirements elicitation in a small-sized software organization: a lightweight implementation of ISO/IEC/IEEE 15939:2017—systems and software engineering—measurement process

Brain and autonomic nervous system activity measurement in software engineering: A systematic literature review

In the past decade, brain and autonomic nervous system activity measurement received increasing attention in the study of software engineering (SE). This paper presents a systematic literature review (SLR) to survey the existing NeuroSE literature. Based on a rigorous search protocol, we identified 89 papers (hereafter denoted as NeuroSE papers). We analyzed these papers to develop a comprehensive understanding of who had published NeuroSE research and classified the contributions according to their type. The 47 articles presenting completed empirical research were analyzed in detail. The SLR revealed that the number of authors publishing NeuroSE research is still relatively small. The thematic focus so far has been on code comprehension, while code inspection, programming, and bug fixing have been less frequently studied. NeuroSE publications primarily used methods related to brain activity measurement (particularly fMRI and EEG), while methods related to the measurement of autonomic nervous system activity (e.g., pupil dilation, heart rate, skin conductance) received less attention. We also present details of how the empirical research was conducted, including stimuli and independent and dependent variables, and discuss implications for future research. The body of NeuroSE literature is still small. Yet, high quality contributions exist constituting a valuable basis for future studies.

An Efficient Model for Software Quality Analysis Based on User and Developer Intraction

Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering may be not an exemption, as those size and complicated nature of software increases, manual examination of software becomes a harder assignment. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis software metrics used to measure the software product and process. In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality will be a method for measuring how software is designed and how well the software conforms to that configuration. A percentage of the variables that we would be searching for software superiority and Correctness, item quality, Scalability, completeness and absence of bugs of those quality standard that might have been utilized from you quit offering on that one association will be unique in relation to others for this reason it may be better to apply the software measurements to measure the quality of software and the current is most common software metrics tools to decrease the partiality of faults during the valuation of software quality. The central influence of this study is an indicationaround software metrics to illustrate for development in this field by critical investigation about key metrics initiated onboth developer and user interactiona unified definition of software quality management on User and Developer (SQMUD) is proposed

Analysis of Software Quality Using Software Metrics

Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.

An automated software reliability prediction system for safety critical software

Software reliability is one of the most important software quality indicators. It is concerned with the probability that the software can execute without any unintended behavior in a given environment. In previous research we developed the Reliability Prediction System (RePS) methodology to predict the reliability of safety critical software such as those used in the nuclear industry. A RePS methodology relates the software engineering measures to software reliability using various models, and it was found that RePS's using Extended Finite State Machine (EFSM) models and fault data collected through various software engineering measures possess the most satisfying prediction capability. In this research the EFSM-based RePS methodology is improved and implemented into a tool called Automated Reliability Prediction System (ARPS). The features of the ARPS tool are introduced with a simple case study. An experiment using human subjects was also conducted to evaluate the usability of the tool, and the results demonstrate that the ARPS tool can indeed help the analyst apply the EFSM-based RePS methodology with less number of errors and lower error criticality.

Fuzzy based ranking of software reliability measures

This research paper summarizes the results of an implementation of fuzzy multilevel methodology to rank software reliability measures. Of late, computer based systems are used largely for monitoring, protecting and to control safety critical systems like nuclear power plants, Aircraft etc. Reliability is an important factor for assessing the performance of such safety critical digital systems. The characteristics of such digital safety critical systems are explicitly or implicitly reflected by software engineering measures. Therefore, with the help of such measures, models can be built to predict the reliability of software applications that run on safety critical systems. It is not necessary that every software engineering measures contribute to predict the reliability, hence they need to be ranked based on their influence on reliability. Since sufficient practical data is not available in literature, expert opinion on the selected software engineering measures contributing to reliability based on criterion has been sought. These expert ratings are aggregated and ranked using Chen’s fuzzy logic based ranking method. As the data involved with this kind of problems are inherently imprecise and inexact, application of fuzzy set theory is very suitable for such situations. The top-ranked software engineering measures can be later used to develop a model to predict reliability of safety critical digital systems.

Personality, emotional intelligence and work preferences in software engineering: An empirical study

ContextThere is an increasing awareness among Software Engineering (SE) researchers and practitioners that more focus is needed on understanding the engineers developing software. Previous studies show significant associations between the personalities of software engineers and their work preferences. ObjectiveVarious studies on personality in SE have found large, small or no effects and there is no consensus on the importance of psychometric measurements in SE. There is also a lack of studies employing other psychometric instruments or using larger datasets. We aim to evaluate our results in a larger sample, with software engineers in an earlier state of their career, using advanced statistics. MethodAn operational replication study where extensive psychometric data from 279 master level students have been collected in a SE program at a Swedish University. Personality data based on the Five-Factor Model, Trait Emotional Intelligence Questionnaire and Self-compassion have been collected. Statistical analysis investigated associations between psychometrics and work preferences and the results were compared to our previous findings from 47 SE professionals. ResultsAnalysis confirms existence of two main clusters of software engineers; one with more “intense” personalities than the other. This corroborates our earlier results on SE professionals. The student data also show similar associations between personalities and work preferences. However, for other associations there are differences due to the different population of subjects. We also found connections between the emotional intelligence and work preferences, while no associations were found for self-compassion. ConclusionThe associations can help managers to predict and adapt projects and tasks to available staff. The results also show that the Emotional Intelligence instrument can be predictive. The research methods and analytical tools we employ can detect subtle associations and reflect differences between different groups and populations and thus can be important tools for future research as well as industrial practice.

Happy software developers solve problems better: psychological measurements in empirical software engineering

For more than thirty years, it has been claimed that a way to improve software developers’ productivity and software quality is to focus on people and to provide incentives to make developers satisfied and happy. This claim has rarely been verified in software engineering research, which faces an additional challenge in comparison to more traditional engineering fields: software development is an intellectual activity and is dominated by often-neglected human factors (called human aspects in software engineering research). Among the many skills required for software development, developers must possess high analytical problem-solving skills and creativity for the software construction process. According to psychology research, affective states—emotions and moods—deeply influence the cognitive processing abilities and performance of workers, including creativity and analytical problem solving. Nonetheless, little research has investigated the correlation between the affective states, creativity, and analytical problem-solving performance of programmers. This article echoes the call to employ psychological measurements in software engineering research. We report a study with 42 participants to investigate the relationship between the affective states, creativity, and analytical problem-solving skills of software developers. The results offer support for the claim that happy developers are indeed better problem solvers in terms of their analytical abilities. The following contributions are made by this study: (1) providing a better understanding of the impact of affective states on the creativity and analytical problem-solving capacities of developers, (2) introducing and validating psychological measurements, theories, and concepts of affective states, creativity, and analytical-problem-solving skills in empirical software engineering, and (3) raising the need for studying the human factors of software engineering by employing a multidisciplinary viewpoint.

A method for assessing the usability of an on screen display for a brain-computer interface

Brain-computer interfaces (BCIs) aim to provide a mechanism by which individuals with severe forms of paralysis can communicate their wishes to others or gain control over devices in their environment. This paper describes the evaluation of an interface designed for use with BCI, which is dedicated to providing a flexible and extensible framework for controlling multiple devices within a domestic environment. A structured format for assessing the usability aspects of this interface is presented. The goal-question-metric (GQM) approach derived from measurement in software engineering was used, providing a mechanism that can be readily adapted to assess other interfaces in assistive technology. The assessment was based on interaction of five participants currently using assistive technology who provided an initial representation of the target user group. Their input, in addition to guidance from a lead user, was used to influence subsequent design choices and directions and highlight the importance of user-centred design within BCI development.

SOFTWARE ENGINEERING JOB PRODUCTIVITY — A SYSTEMATIC REVIEW

Productivity is a key element in organizational management. Although it can be measured at different levels (country, sector, organization …) this research focuses on productivity at the job level. The aim of this paper is to obtain an overview of the state of the art in productivity measurement in software engineering, including the inputs and outputs of the production process used for this measurement at the job level in the workplace. To do so, a systematic literature review protocol was adapted from literature review protocol standards, and subsequently carried out. The objective is to assess the current inputs and outputs present in the literature in order to create new productivity measures for software practitioners. This paper reveals that two different measures are used to assess software engineering professionals: traditional SLOC/Time and planning projects units per time unit.

Relational Analysis of Software Developer’s Quality Assures

Software engineering approach ensures the quality of software and the delivery of the product on to time to their client. The development process is facing many challenges to optimize the resource and provide the delivery on time. Each phase of the development has its unique functionality. Software development model and the architectural approaches are differ that will creates an impact in the quality of the delivery .however the role and their skill sets are vital role in the developmental process and to meet the deadlines of the development tasks. The developer's skills and the developmental tasks inline to the functional domain and its relational process are determine the quality factors such minimum error, functional correctness and user satisfactory. This research attempted observes the functional process of the development sector and determine the influencing factor and its associative relationship. The data from the industry and its descriptive analysis with the determined factors are discussed as part of the paper. I. Introduction The success factor of any software development system highly depends upon the architecture of the software development model, technical reliability used in it, pre-planning and preparatory works done and the effective implementation of the specified works in a time driven manner along with the developers completion. In the same way Software architecture forms the backbone for building successful software-intensive systems. Architecture largely permits or precludes a system's quality attributes such as performance or reliability. In general, a critical issue in the design and construction of any complex software system is based on its architecture implementation by the developers. The right architecture is the key to software project success. Conversely, the wrong one is a sure road to failure. A good architecture can help ensure that a system will satisfy key requirements in such areas as performance, reliability, portability, scalability, and interoperability. This paper attempted to relate the software architecture and its development model along with the developers role. The developer efficiency and related factors are addressed to develop the model for effective software development process and its metrics. Software development process is a sequence of activities. These activities are dependent with one another in each level. These activities are related in the next phase. It is a chain of activities involved to determine the resource allocation, utilization and coating process. These relationship activities are used to determine the software process reusability and maintenance. This dependent and independent activities are varies as per the architecture in the software model of development phases. This research process tinted to determine the dependent and regression process values to identify the model and architecture. These create an effect in the cost, development, resource identification and optimization etc. II. Software Engineering Measures

Effectiveness of Software Metrics for Object-oriented System

Measurement is a buzzword applied to each and every field of engineering for the development of hardware or firmware products, and software engineering is nowhere an exception. Measurement in software engineering can be best described in terms of metrics, as a quantitative measure of degree to which a system, a component or process possesses a given attribute. The urge for software metrics allows the analysts, designers, coders, testers and the managers to conceive the software development processes and assess the system. The objective of this paper is to evaluate software like ATM using available subset of metrics from traditional and object-oriented methodology. The traditional metrics such as cyclomatic complexity, size and comment percentage are used to compute the software complexity. This paper also analyses a widely used subset of object-oriented metrics such as the Chidamber and Kemerer metric suite to compute the system reliability. The metric values are evaluated for a real life application, which helps us to know the complexity and the reliability of the ATM software.

Performance Management in Software Engineering

Performance measurement in software engineering has to meet a multiplicity of challenges. Oftentimes, traditional metrics focus on sequential development instead of using incremental and iterative development. Output is measured on a pure quantitative (e.g., SLOC), quality-disregarding basis. A project’s input is hard to assign properly using enterprise-unspecific forecasting tools which have to be calibrated at first and which do not account for time preferences. Requirements necessary for behaviourally adjusted project management and control are rarely discussed. Focusing on these shortcomings, this paper proposes an enterprise-specific approach which combines lifecycle and activity based costing techniques for software development following the incremental and iterative Unified Process model. Key advantages are calibration effort can be avoided, project management decisions are supported by a clear managerial accounting emphasis, precise milestone-depending cost objectives can be determined as the basis for personnel management and control of development teams, and cost and time variance analysis can be supported in a sophisticated way.

On the Cognitive Complexity of Software and its Quantification and Formal Measurement

The quantification and measurement of functional complexity of software are a persistent problem in software engineering. Measurement models of software complexities have been studied in two facets in computing and software engineering, where the former is machine-oriented in the small; while the latter is humanoriented in the large. The cognitive complexity of software presented in this paper is a new measurement for cross-platform analysis of complexities, functional sizes, and cognition efforts of software code and specifications in the phases of design, implementation, and maintenance in software engineering. This paper reveals that the cognitive complexity of software is a product of its architectural and operational complexities on the basis of deductive semantics. A set of ten Basic Control Structures (BCS’s) are elicited from software architectural and behavioral modeling and specifications. The cognitive weights of the BCS’s are derived and calibrated via a series of psychological experiments. Based on this work, the cognitive complexity of software systems can be rigorously and accurately measured and analyzed. Comparative case studies demonstrate that the cognitive complexity is highly distinguishable for software functional complexity and size measurement in software engineering.

ANN model for predicting software function point metric

Software Engineering measurement and analysis specially, size estimation initiatives have been in the center of attention for many firms. Function Point (FP) metric is among the most commonly used techniques to estimate the size of software system projects or software systems for measuring the functionality delivered by a system. In this paper we explore an alternative, Artificial Neural Network (ANN) approach for predicting function Point. We proposed an ANN model to explore neural network as tool for function point metric. A multilayer feed forward network is trained using backpropogation algorithm and demonstrated to be suitable. The training and validation data is randomly selected from the data repository of 365 projects [7]. The experimental results of two validation sets each of 55 projects indicate that the Mean Absolute Relative Error (MARE) was 0.198 and 0.145 of ANN model and shows that ANN model is a competitive model as Function Point Metric.

A Quantity Model for Controlling and Measuring Software Quality Based on the Expert Decision-Making Algorithm

Researchers have been active in the field of software engineering measurement over more than 30 years. The software quality product is becoming increasingly important in the computerized society. Target setting in software quality function and usability deployment are essential since they are directly related to development of high quality products with high customer satisfaction. Software quality can be measured as the degree to which a particular software program complies with consumer demand regarding function and characteristics. Target setting is usually subjective in practice, which is unscientific. Therefore, this study proposes a quantity model for controlling and measuring software quality via the expert decision-making algorithm-based method for constructing an evaluation method can provide software in relation to users and purchasers, thus enabling administrators or decision makers to identify the most appropriate software quality. Importantly, the proposed model can provide s users and purchasers a reference material, making it highly applicable for academic and government purposes.

A management information system for mine railway transportation equipment

Abstract Good equipment management is essential for the day to day management of an enterprise. Targeted at production operation of the railway transportation department of a mining group and aimed at mine railway equipment management, we have established a management information system for the equipment in the entire process of the life cycle of equipment. The project deals with basic data about equipment, initial management, maintenance, operation and even disposal, based on a C/S and B/S structure. We adopted an object-oriented approach, dealing with software engineering, information engineering, economic and organizational measures. Thus, effective monitoring and control of the operation of railway equipment and its status in the entire process has been achieved.

MEASUREMENT IN SOFTWARE ENGINEERING: FROM THE ROADMAP TO THE CROSSROADS

Research on software measurement can be organized around five key conceptual and methodological issues: how to apply measurement theory to software, how to frame software metrics, how to develop metrics, how to collect core measures, and how to analyze measures. The subject is of special concern for the industry, which is interested in improving practices — mainly in developing countries, where the software industry represents an opportunity for growth and usually receives institutional support for matching international quality standards. Academics are also in need of understanding and developing more effective methods for managing the software process and assessing the success of products and services, as a result of an enhanced awareness about the emergency of aligning business processes and information systems. This paper unveils the fundamentals of measurement in software engineering and discusses current issues and foreseeable trends for the subject. A literature review was performed within major academic publications in the last decade, and findings suggest a sensible shift of measurement interests towards managing the software process as a whole — without losing from sight the customary focus on hard issues like algorithm efficiency and worker productivity.

Software metrics for enhanced business excellence: An investigation of research issues from a macro perspective

Software measurement is a challenging as well as an essential element of a wholesome and highly efficient software engineering culture. ‘Software metrics’ refers to the collective term underlying the wide range of activities associated with measurement in software engineering. There are numerous taxonomies to organize software metrics (based on entities, type of measure etc), but all of them fundamentally fit into three broad domains, namely resource, process and product metrics. The number of metrics in each of these categories is ever increasing with the result that practitioners are overwhelmed with data as they get quantitative inputs on a variety of attributes. This poses a real problem for decision-makers and analysts as many times it is unclear which attributes should be managed so as to augment the overarching goal of productivity or quality improvement. This paper aspires to address the research issues with respect to ably managing the subject of software metrics in a structured and coordinated manner. The present work deals with the concept of software measurement and metrics and argues that a great degree of simplification is required as, otherwise, the cornucopia of metrics would defeat the very purpose of the measurement approach, which is to quantify the key characteristics of interest in order to extract meaningful inferences about them and their relationships with one another. A cause-analytic model is also proposed, which if validated empirically would help to throw light on certain key metrics that could form the foundation for the further realization of the measurement and metrics approach. To encapsulate, the proposed research adopts a holistic approach in that it encompasses all categories of metrics in its analysis and it takes a macro-perspective in the sense that the sole purpose is to better organize the body of research with respect to software metrics.