Software Project Control Research Articles

Software Measurements Using Machine Learning Techniques - A Review

Background: Software Measurement (SM) is pivotal for efficient planning, scheduling, tracking, and controlling software projects, which significantly affects the success or failure of a project. Machine Learning (ML) techniques have been applied for software measurements for the past three decades. Objective: This review aims to synthesize the studies conducted from the years 1990 to 2020 to provide a broad picture of the role of machine learning in the world of software measurement. Method: The Systematic Literature Review (SLR) approach is adopted to conduct this review. Inclusion/exclusion criteria are defined to select the most relevant studies. The researcher searched the prominent databases and archives and obtained around 2310 studies, from which 108 studies were selected as primary studies, which were then summarized to accomplish the goals of this review. Results: The distinguished contribution of this review is that it covers all aspects of software measurements from the perspective of the application of machine learning techniques. It guides the software practitioners regarding the journey of software measurements to date using machine learning techniques in a single synthesized study. It further provides future guidelines for the researchers working in this field. Conclusion: Machine learning techniques have extensive applications for software measurements. Software fault prediction and software effort estimation are the most prevailing SM tasks harnessing the ML techniques. The most popular ML technique is the artificial neural network for SM. For empirical studies, NASA and promise datasets are extensively used. Over the last decade (2011-2020), SM paradigm has been shifting towards ensembles of individual ML models and deep learning models.

Integrated revision and variation control for evolving model-driven software product lines

Software engineering projects are faced with abstraction, which is achieved by software models, historical evolution, which is addressed by revision control, and variability, which is managed with the help of software product line engineering. Addressing these phenomena by separate tools ignores obvious overlaps and therefore fails at exploiting synergies between revision and variation control for models. In this article, we present a conceptual framework for integrated revision and variation control of model-driven software projects. The framework reuses the abstractions of revision graphs and feature models and follows an iterative, revision-control-like approach to software product line engineering called product-based product line development. A single version (i.e., a variant of a selected revision) is made available in a workspace, where the user may apply arbitrary modifications. Based on a user-provided specification of the affected variants, the changes are automatically written back to a transparent repository that relies on an internal multi-version storage. The uniform handling of revisions and variants of models is achieved by transparently mapping version concepts to a semantic base layer, which is defined upon propositional logic. At the heart of the conceptual framework is a dynamic filtered editing model, which allows that the versioned artifacts and the feature model co-evolve. We contribute algorithms for checkout and commit, which satisfy a set of consistency constraints referring to variant specifications in an evolving feature model. This article furthermore addresses the orchestration of collaborative development by distributed replication and the well formedness of text and model artifacts to be checked out into the workspace. The Eclipse-based tool SuperMod demonstrates the feasibility of the conceptual framework. It allows the user to reuse arbitrary editing tools for text-based programming and/or Ecore-based modeling languages. An evaluation based on three case studies investigates the properties of SuperMod with a specific focus on filtered editing. The evaluation demonstrates that the dynamic filtered editing model reduces the cognitive complexity and the amount of user interaction necessary for variation control when compared to unfiltered model-driven approaches to software product line engineering.

Software Analytics: What’s Next?

Knowing what factors control software projects is very useful because humans might not understand those factors. Developers sometimes develop their own ideas about good and bad software, on the basis of just a few past projects. Using software analytics, we can correct those misconceptions. Software analytics lets software engineers learn about AI techniques. Once they learn those techniques, they can build and ship innovative AI tools. That is, software analytics is the training ground for the next generation of AI-literate software engineers. This article is part of a special issue on software engineering’s 50th anniversary.

Entailment-Based Intelligent System for Software Project Monitoring and Control

In recent years, software project managers compare actual completion of activities against the progress reports filled by project members to identify significant deviations from the estimated schedules and manage software project risks. However, quantitative measurements are limited due to the format of project documents, which are mostly natural languages. In this paper, we propose an intelligent system for software project monitoring and control by using natural language processing techniques to recognize textual entailment of progress reports to further evaluate the level of project fulfillment in a qualitative manner. Our experimental results demonstrate that the proposed method can recognize entailment from text efficiently and outperform other textual entailment approaches. Moreover, we successfully apply the textual entailment technique to project monitoring and control, which not only reduces the project cost and human's effort but also provides a basis for project managers to qualitatively evaluate the performance of each project member.

Empirical Study of Software Metrics

Software metrics are applied to get comparable values of the degree of particular software quality features. These are key “facts” that test managers use to understand their current position and to prioritize their activities to reduce the risk of schedule over-runs on software releases and can control software projects better. Metrics helps to measure current performance. This empirical study was conducted in various IT organizations. The objective of this study is investigation of frequentl ly used software metrics in the IT organizations nowadays. Based on t the detailed investigation, these are divided into four main groups: organization metrics, project metrics, process metrics and product metrics. Product metrics are used often in IT Industry now days.

METRIC BASED SOFTWARE PROJECT PERFORMANCE MONITORING MODEL

Successful implementation of software projects development is entirely depending upon successful monitoring and control mechanism. Software metrics can deliver the necessary information for monitoring and control the software projects development for its enhancement. However, the current software metrics does not widely address the performance criteria and related metrics for software project management. Largely, metrics are identified in the perspectives of software development only. Hence, the aim of this study is to formulate a Metric based Software Project Performance Monitoring Model which consists of performance criteria and metrics that involves in a software projects development. This model formulation is consists of five processes: metrics integration, metrics validation, metrics description, metrics categorization and metrics threshold. The proposed model is a novel approach and adds significant of knowledge to the software engineering domain especially on software project monitoring and software measurement domain. Generally, this model will be a guideline for software project managers to monitor and control software projects particularly in public sector software projects. In order to demonstrate the applicability of this model, case study was conducted at various departments at Malaysian Public Sector. The results show that the proposed model is very useful for the project managers in monitoring and control software projects.

A dynamic decision making method with discrimination of alternatives using associative aggregation operators

The dynamic multicriteria decision-making is focus on select the best alternative according to valuations of several criteria provided at multiple periods, therefore time dimension is considered in the solving process. In this work a dynamic decision making method is proposed which applied a based on associative aggregation operators mechanism to model different behaviors or viewpoints of decision makers. The method consists of four algorithms that enclose the main phases in the solution of a dynamic problem: to calculate the static and dynamic evaluations of alternatives, to calculate the time discrete variable index of alternatives and to use these values to obtain a final ranking. To show its feasibility, the proposal is applied to dynamic control of software projects in order to analyze several indicators at different decision moments and provide a holistic evaluation of the projects management considering their evolution.

Analysis of Multilevel Risk Measurement Algorithm of Software Projects Based on Grey Variable Weight Clustering Model

The development process of the software system is influenced by multi factors, thus the risk control of software projects is of vital importance. Targeting at the features including multi-factor, multilayer, fuzziness and uncertainty during the process of risk assessment of software projects, this paper analyzed the risk measurement of software projects and proposed a multilevel risk measurement algorithm of software projects based on grey variable weight clustering model. This algorithm firstly analyzed the influencing factors of the risk control of software projects, establishing a multilevel risk measurement system for software projects; next, establishing the whitenization weight functions of different measurement indexes and different measurement grades of software projects risks respectively on the basis of the grey system theory, and under the condition of considering the degree of importance of the indexes of different levels, the corresponding risk measurement algorithm of software projects based on grey variable weight clustering model was established; then the paper presented the detailed execution steps of this algorithm and discussed the detailed process to achieve the algorithm. Finally, the algorithm was verified through the case study.

Dynamic monitoring and control of software project effort based on an effort buffer

The improvement to the monitoring and control efficiency of software project effort is a challenge for project management research. We propose to overcome this challenge through the use of a model for the buffer determination and monitoring of software project effort. This software project effort buffer was originally determined on the basis of a risk management factor analysis with total consideration for project managers’ risk preference. The effort buffer was next allocated to different stages according to the buffer allocation cardinal. An effort deviation monitoring and control model was then established based on the grey prediction model, including the establishment of a deviation monitoring and control model, a simulation test of the accuracy and the deviation prediction algorithm flow chart. The method system was eventually applied to an actual project and compared with the actual project data. The results show that the relative error test accuracy of the proposed model is qualified according to the test standard of the grey model, signifying that it could be used for the prediction of effort deviation and decision-making. The proposed model could use the dynamic control system to monitor and control software project effort in an effective manner.

Organizing Software Project Control Practices

The major challenge for software project control is to adapt to the associated control practices in order to achieve sustainability for the software project. This paper reviews software project control practices in a number of control perspectives, namely organizational controls, project management, software engineering and cybernetic management. The aim is to extend the concept of controlling software project towards adapting appropriate control practices. A number of basic control components are identified to better understand the control practices. We found that (a) control elements create its own complexities on software project control; (b) communication between the controller and controlee is important in identifying and understanding control direction; (c) each individual has their own roles in practicing software project control; and (d) there is a needs for organizing complexities in managing software project control practices. The principal recommendation is to understand proper control practices in studies of various complex situations of software project control.

Goal‐oriented customization of software cockpits

AbstractSoftware cockpits, also known as Software Project Control Centers, support the management and controlling of software and system development projects and provide means for quantitative, measurement‐based project control. Currently, many companies are developing simple control dashboards that are mainly based on Spreadsheet applications. Alternatively, they use solutions providing a fixed set of project control functionalities that cannot be sufficiently customized to their specific needs and goals. Specula is a systematic approach for defining reusable, customizable control components and instantiating them according to different organizational goals and characteristics based on the Quality Improvement Paradigm (QIP) and the Goal Question Metric (GQM) approach. This article gives an overview of the Specula approach, including the basic conceptual model, goal‐oriented composition of control centers based on explicitly stated measurement goals and a conceptual architecture supporting the approach. Related approaches are discussed, a practical usage example is given, and evaluation results from using Specula as part of industrial case studies are presented. Copyright © 2010 John Wiley & Sons, Ltd.

Software project control centers: concepts and approaches

On-line interpretation and visualization of project data are gaining increasing importance on the long road towards predictable and controllable software project execution. In the context of software development, only few techniques exist for supporting these tasks. This is caused particularly by the often insufficient use of engineering principles in the software development domain. Beyond that, interpretation and visualization techniques from other domains (such as business or production processes) are not directly applicable to software processes because of the specific characteristics of software development. A software project control center (SPCC) is a means for collecting, interpreting, and visualizing measurement data in order to provide purpose- and role-oriented information to all involved parties (e.g., project manager, quality assurer) during the execution of a project. This article presents a reference model for concepts and definitions around SPCCs. Based on this reference model, a characterization and classification of essential approaches contributing to this field is given. Finally, an outline for future research is derived from identified deficiencies of existing approaches.

Simple Metrics for Improving Software Process Performance and Capability: A Case Study

This paper presents a case study which describes the application of statistical process performance monitoring and capability assessment on a large process control software project. The process performance and capability monitoring used in the case study is part of a Quality Framework for Software Development (QFSD) devised for the development of control systems at Fisher–Rosemount Systems and provides a practical alternative to ISO9000 quality models. The process performance measurements were based on commonly available metrics that could be obtained with the minimum disruption to the processes being examined. The application of statistical methods were used to establish problem areas at the earliest opportunity allowing process adjustments to be made to improve the process performance. The processes used were defined in terms of work products which were categorized and evaluated for the level of completion. This allowed a process capability to be calculated. Those processes which were found to be at a lower capability level became the focus for the process improvement for the next project. In particular the capability measurement identified processes where questions needed to be raised about the relevance of the process, whether the techniques used were effective and whether the tools were adequate. It was found that achieving high performance and capability is a learning process with the development team improving with each new project. The benefits have been better managed, more cost and time effective projects producing higher quality software. Management and the development team have obtained a better understanding of the software development process and this continuing learning process has lead to a continuous improvement in both the development methodology and the resulting software. The paper presents examples of the real benefits that can be obtained by tracking process performance and assessing process capability at all stages, and shows that this can be achieved without the need to resort to complex procedures for process measurement, as most of the empirical data came from commonly available process data.

Software project control and metrics

With the aim of providing scientific guidance for the application of diamond cutting tools to the machining of stone-plastic composite, this work presents results on the influence of tool geometry and cutting parameters on cutting forces and temperature during helical milling of stone–plastic composite with diamond cutters. Four factors—helical angle, spindle speed, feed rate, and cutting depth—were assessed using a response surface method. Mathematical models were developed and identified by verification testing to accurately predict changes in cutting forces and temperature during composite helical milling. Then, the significant contributions of each factor and of two-factor interactions were determined by analysis of variance, and the trends of cutting forces and temperature were studied using response surface methodology. The optimal conditions in terms of low cutting forces and temperature were determined to be a helical angle of 70°, cutting speed of 51.3 m/s, feed per tooth of 0.24 mm, and cutting depth of 0.5 mm. These parameters are proposed for use in the industrial production of stone–plastic composite material to improve machining efficiency.

Process control software projects: you CAN manage Presented at the World Batch Forum (previously presented at Fisher-Rosemount User Group, November, 1995)

Software projects in process automation have been proven difficult to manage. Technology trends promise that in the future software will consume considerably more energy than it does now. Business challenges us to deliver what the customer needs at a reduced cost, and faster than we've done it before. What's an automation engineer to do! This presentation will share approaches to improve the software delivery process. We are enabled by tools and resources like automated development techniques, modular approaches, resources from industry and academia, and fueled by recognition and rewards. Metrics help us benchmark our performance as we attempt improvement and compare our performance and contributions with others. Human factors are still incredibly significant in insuring success. We'll discuss each of these factors with examples of how they've been used to influence improvement.

Software engineering for ESO's VLT project

This paper reports on the experience at the European Southern Observatory on the application of software engineering techniques to a 200 man-year control software project for the Very Large Telescope (VLT). This shall provide astronomers, before the end of the century, with one of the most powerful telescopes in the world. From the definition of the general model, described in the software management plan, specific activities have been and will be defined: standards for documents and for code development, design approach using a CASE tool, the process of reviewing both documentation and code, quality assurance, test strategy, etc. The initial choices, the current implementation and the future planned activities are presented and, where feedback is already available, pros and cons are discussed.

Fuzzy systems and neural networks in software engineering project management

To make reasonable estimates of resources, costs, and schedules, software project managers need to be provided with models that furnish the essential framework for software project planning and control by supplying important “management numbers” concerning the state and parameters of the project that are critical for resource allocation. Understanding that software development is not a “mechanistic” process brings about the realization that parameters that characterize the development of software possess an inherent “fuzziness,” thus providing the rationale for the development of realistic models based on fuzzy set or neural theories.

Software project control: an experimental investigation of judgment with fallible information

Software project management is becoming an increasingly critical task in many organizations. While the macro-level aspects of project planning and control have been addressed extensively, there is a serious lack of research on the micro-empirical analysis of individual decision making behavior. The heuristics deployed to cope with the problems of poor estimation and poor visibility that hamper software project planning and control are investigated, and the implications for software project management are examined. A laboratory experiment in which subjects managed a simulated software development project is reviewed. The subjects were given project status information at different stages of the lifecycle and had to assess software productivity in order to dynamically readjust project plans. A conservative anchoring and adjustment heuristic is shown to explain the subjects' decisions quite well. Implications for software project planning and control are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Organizing software information

When developing, implementing, and maintaining large-scale information systems, many facets of knowledge become relevant. Based on experiences in ESPRIT project DAIDA, we analyze these facets and sketch a comprehensive formal model of knowledge relevant to IS development and maintenance. Such a model can be used to structure development environments, to discuss and control software projects, and to support design decisions and their tool-assisted execution in CASE environments. Based on this model, we have implemented a knowledge-based and decision-oriented software information system called ConceptBase which provides efficient management of large bases of conceptual models and tools for exploratory analysis and (group) decision-making in IS engineering.

Asset-R: A function point sizing tool for scientific and real-time systems

This paper presents the results of experiments conducted over the past three years to empirically validate extensions made to enable function point theory to handle scientific and real-time systems. Function points are a specification-based measure of software size that do not depend on lines of code estimates. The paper begins by presenting necessary background information on the theory of function points and the fundamental mathematics upon which it is premised. It then summarizes the strengths and weaknesses of the technique and describes how they were handled as the Analytical Software Size Estimation Technique (ASSET) was formulated and validated statistically. The paper next reports the results of experiments conducted during the past three years to further validate the methodology. The fidelity of these predictions were measured by comparing the size estimates developed from requirements specifications to actuals realized on variety of data processing, scientific, and real-time systems. The paper continues by describing the Analytical Software Size Estimation Technique-Real-Time (ASSET-R) package and shows how it effectively uses expert system concepts to let users access its knowledge base of counting conventions and complexity bounding rules. Finally, the paper concludes by discussing how function points can be used in the future to improve estimating and control of software projects.