In the rapidly evolving landscape of modern education, Science, Technology, Engineering and Mathematics (STEM) disciplines stand out for their role in equipping students with the skills necessary to address complex real-world problems. Despite the critical importance of STEM careers, disparities persist, particularly among young women and students from public schools. This motivated the creation of an IoT bootcamp - referred to as PyTime IoT, a 2-day course aimed at high school students. This bootcamp integrates hands-on activities in Python programming and IoT systems that demonstrate the tangible application of theoretical STEM concepts through real-world scenarios. Our approach enhances participants' understanding of IoT and programming and also serves as a stepping-stone for high school students contemplating STEM careers. Preliminary results from the first two bootcamp editions indicate a positive shift in participants' skills and a marked increase in STEM career interest, suggesting that targeted bootcamps like PyTime IoT are effective in bridging the educational gap and inspiring the next generation of STEM professionals.

Embedded systems omnipresent in everyday life and industry are mainly composed of hardware and software that must comply with a number of standards and regulations. However, there is no consensus on the quality characteristics and subcharacteristics of embedded software. This article presents the steps for modeling an operational quality model for embedded software aligned with the ISO 25000 series of quality models for traditional computer systems. From a literature review composed of 40 studies on quality modeling for embedded systems and software, 85 of the most frequent quality characteristics and subcharacteristics were first identified, including a subset of 16 referenced or cited in at least 25% of the literature. Next, the design of a quality model for embedded software aligned with the ISO 25000 series was proposed with 13 characteristics and 27 subcharacteristics. The operational aspect of this quality model for embedded software is addressed next through a set of measures and measurement functions from ISO 25000 to aggregate the results of the quantification of the characteristics and subcharacteristics. A survey involving 25 embedded software specialists is presented next to gauge, using Fleiss's Kappa criteria, their agreement with the proposed quality model. Furthermore, the computed importance weights derived from the survey participants’ individual opinions were compared with those derived from an analysis of 40 embedded software studies, bolstering the credibility of the model. The results of this study suggest that the proposed quality model can serve as a framework for evaluating and understanding the quality characteristics across diverse expertise levels. Furthermore, the convergence between the survey and the literature strengthens the model's credibility by anchoring it in both established literature and practitioners’ agreements.

Abstract Context Software development effort estimation (SDEE) is one of the most challenging aspects in project management. The presence of missing data (MD) in software attributes makes SDEE even more complex. K‐nearest neighbors imputation (KNNI) has been widely used in SDEE to deal with the MD issue. However, KNNI, in its classical process, has low tolerance to imprecision and uncertainty especially when dealing with categorical features. When dealing with categorical attributes, KNNI uses a classical approach, employing mainly numbers or classical intervals to represent software attributes and similarity measures originally designed for numerical attributes. Objectives This paper evaluates the use of an optimized fuzzy clustering‐based KNNI (FC‐KNNI) and compares it with classical KNN when dealing with mixed data in the context of SDEE. Methods We investigate the effect of two imputation techniques (FC‐KNNI and KNNI) on five SDEE techniques: case‐based reasoning, fuzzy case‐based reasoning, support vector regression, multilayer perceptron, and reduced‐error pruning tree. The evaluation is carried out using six publicly available datasets for SDEE using two performance measures, standardized accuracy (SA), and Pred (0.25). The Wilcoxon statistical test is also performed to assess the significance of results. Results The results are promising in the sense that using an imputation technique designed for mixed data is better than reusing methods originally designed for numerical data. We found that FC‐KNNI significantly outperforms KNNI regardless of the SDEE technique and dataset used. Another important finding is that F‐CBR improved the analogy process compared to CBR. Conclusion The introduction of fuzzy sets and fuzzy clustering in the analogy process improves its performances in terms of SA and Pred (0.25).

Abstract Software maintenance is a challenging and laborious software management activity, especially for open‐source software. The bugs reports of such software allow tracking maintenance activities and were used in several empirical studies to better predict the bug resolution effort. These reports are known for their large size and contain nonrelevant instances that need to be preprocessed to be suitable for use. To this end, instance selection (IS) has been proposed in the literature as a way to reduce the size of the datasets, while keeping the relevant instances. The objective of this study is to perform an empirical study that investigates the impact of data preprocessing through IS on the performance of bug resolution prediction classifiers. To deal with this, four IS algorithms, namely, edited nearest neighbor (ENN), repeated ENN, all‐k nearest neighbors, and model class selection, are applied on five large datasets, together with five machine learning techniques. Overall, 125 experiments were performed and compared. The findings of this study highlight the positive impact of IS in providing better estimates for bug resolution prediction classifiers, in particular using repeated ENN and ENN algorithms.

In software engineering, maintenance effort estimation is a challenging research topic. Several empirical studies have focused on Maintenance Effort Estimation for Open Source Software (O-MEE) using Machine Learning (ML) techniques. Although Tuning the Parameters (TP) of ML techniques has proved to improve their performance, it has not yet been investigated in the context of O-MEE for bug resolution prediction. Therefore, this empirical study investigates the impact of grid search TP method, on the performance of k-Nearest Neighbor, Support Vector Machine, and Random Tree using Eclipse Platform, Eclipse JDT, and Apache datasets. A set of 18 ML classifiers were built and the results show the positive impact of the TP on the ML techniques' performance compared to the default ones.

Studies about maintenance effort estimation of open-source software have investigated the impact of single instance selection on the performance machine learning techniques. However, Ensemble of Instance Selection (EIS) has not been investigated for this topic, especially for bug resolution prediction. This empirical study considers the impact of EIS on the performance of k-Nearest Neighbor, Support Vector Machine, and Multinomial Naïve Bayes techniques. A set of 27 classifiers are built using Bagging and Random Feature Subset Ensembles based on AllkNN single instance selection algorithm on three datasets. The results are presented, together with a comparative analysis of the built classifiers' performance. The results show that the investigated EIS algorithms improve the performance of ML classifiers and outperform the single instance selection algorithm-based classifiers.

Abstract Software development effort estimation (SDEE) remains as the principal activity in software project management planning. Over the past four decades, several methods have been proposed to estimate the effort required to develop a software system, including more recently machine learning (ML) techniques. Because ML performance accuracy depends on the features that feed the ML technique, selecting the appropriate features in the preprocessing data step is important. This paper investigates three filter feature selection techniques to check the predictive capability of four single ML techniques:K‐nearest neighbor, support vector regression, multilayer perceptron, and decision trees and their homogeneous ensembles over six well‐known datasets. Furthermore, the single and ensembles techniques were optimized using the grid search optimization method. The results suggest that the three filter feature selection techniques investigated improve the reasonability and the accuracy performance of the four single techniques. Moreover, the homogeneous ensembles are statistically more accurate than the single techniques. Finally, adopting a random process (i.e., random subspace method) to select the inputs feature for ML technique is not always effective to generate an accurate homogeneous ensemble.

The development of advanced mobile applications, which combines the use of IoT hardware and cloud computing services, is challenging in an academic environment; specialized knowledge of teachers in such subjects is required, as well as appropriate management of multidisciplinary groups of students with different knowledge and skills. This paper presents an educational model (CEMA Cloud-based Educational model for Mobile Application) where cloud computing services are used for developing mobile applications that are connected to IoT devices. The model includes the development of educational resources such as micro-training and lab sessions aimed to facilitate the learning process of IoT and Cloud Mobile Applications. Results indicate that students consider Github and Asana as tools easy to use. Also, students highly value the practical activities which are perceived as useful educational resources for achieving a high degree of competencies in managing projects using an agile methodology, Android programming and handling IoT sensors.

Abstract Missing data is a serious issue in software engineering because it can lead to information loss and bias in data analysis. Several imputation techniques have been proposed to deal with both numerical and categorical missing data. However, most of those techniques used is simple reuse techniques originally designed for numerical data, which is a problem when the missing data are related to categorical attributes. This paper aims (a) to propose a new fuzzy case‐based reasoning (CBR) imputation technique designed for both numerical and categorical data and (b) to evaluate and compare the performance of the proposed technique with the k‐nearest neighbor (KNN) imputation technique in terms of error and accuracy under different missing data percentages and missingness mechanisms in four software engineering data sets. The results suggest that the proposed fuzzy CBR technique outperformed KNN in terms of imputation error and accuracy regardless of the missing data percentage, missingness mechanism, and data set used. Moreover, we found that the missingness mechanism has an important impact on the performance of both techniques. The results are encouraging in the sense that using an imputation technique designed for both categorical and numerical data is better than reusing methods originally designed for numerical data.

Missing data are a serious issue that influences the prediction accuracy of software development effort estimation (SDEE) techniques and especially analogy-based software effort estimation (ASEE). Hence, appropriate handling of missing data is necessary in order to ensure best performance. To deal with this issue K-nearest neighbors (KNN) imputation has been widely used. However, none of the studies investigating KNN imputation in SDEE have addressed the impact of parameter settings on the imputation process given that parameter optimization techniques are often used at the prediction level, as they highly impact the performance of SDEE techniques including ASEE. This paper proposes and evaluates an ensemble KNN imputation technique for ASEE. Thereafter, we compare ASEE performance using ensemble KNN imputation with those using either a grid search based single KNN imputation or KNN imputation without parameter optimization. For the six datasets used for comparison, the ensemble KNN imputation significantly improved ASEE performance compared with KNN imputation without optimization. Moreover, ensemble KNN imputation and grid search-based imputation behaved similarly. Given that grid search is time consuming, the ensemble KNN imputation may be an alternative to deal with missing data in the ASEE process.

Abstract Analogy‐based estimation is one of the most widely used techniques for effort prediction in software engineering. However, existing analogy‐based techniques suffer from an inability to correctly handle nonquantitative data. To deal with this limitation, a new technique called 2FA‐kprototypes was proposed and evaluated. 2FA‐kprototypes is based on the use of the fuzzy k‐prototypes clustering technique. Although fuzzy k‐prototypes algorithms are well known for their efficiency in clustering numerical and categorical data, they are sensitive to the selection of initial cluster centers. In this paper, the impact of cluster center initialization on improving the prediction accuracy of 2FA‐kprototypes was analyzed and discussed using two cluster initialization techniques: centrality‐based initialization and density‐based initialization. The performance of 2FA‐kprototypes using these two initialization techniques was evaluated and compared with that of 2FA‐kprototypes using random initialization over four datasets: ISBSG, COCOMO81, USP05‐FT, and USP05‐RQ. The results showed an improvement in the performance of 2FA‐kprototypes in terms of estimation accuracy when the all‐in method is used.

Abstract Researchers have developed and evaluated many techniques to deliver accurate estimates of the effort required to complete a new software program. Among these, analogy has emerged as a very promising technique, in particular the fuzzy analogy estimation technique that uses the fuzzy logic concepts in order to deal with both categorical and numerical data. The aim of this paper is twofold: (1) evaluate the impact of 3 filters on the predictive ability of single and ensemble fuzzy analogy techniques and (2) assess whether filters could be a source of diversity for fuzzy analogy ensembles. Moreover, it compares the filter single and ensemble fuzzy analogy techniques with fuzzy analogy ensembles built without using feature selection over 6 datasets. The overall results suggest that (1) more accurate estimates are generated when filters were used with single and ensemble fuzzy analogy techniques, (2) filter single fuzzy analogy techniques outperformed filter fuzzy analogy ensembles, and (3) fuzzy analogy ensembles without feature selection were more accurate than filter single and ensemble techniques. Therefore, though the use of feature selection techniques led single and ensemble fuzzy analogy to generate accurate estimations, they failed to be a source of diversity for fuzzy analogy ensembles. Hence, constructing fuzzy analogy homogenous ensembles that combine single fuzzy analogy techniques with different parameter configurations still generate better accuracy than filter fuzzy analogy ensembles. However, further empirical evaluations of filter/wrappers fuzzy analogy ensembles are required in order to confirm or refute these findings.

Abstract As effort estimation has gained increasing attention, most of the techniques proposed have focused on the accuracy of effort estimates. Yet no clear conclusions have been drawn on which techniques perform best in all contexts. We propose an entropy‐based approach to effort estimate variation caused by measurement and model error sources whatever the effort estimation technique used. The proposed approach was empirically evaluated by exploring three entropy formulae, four interpolation methods, and two analogy‐based effort estimation approaches (crisp and fuzzy analogy) over seven datasets using the Jackknife evaluation method. The obtained results show that the three entropy formulae have in general the same positive influence on the performance of the entropy‐based approach measured in terms of absolute error of effort deviation. In addition, the spline interpolation outperformed all other interpolation methods, using any of the entropy formulae. Moreover, achievement percentages of the best variants of our approach closely approximated those of the Gaussian distribution confirming that the Gaussian distribution is useful for characterizing effort estimate variation.

Abstract Software development effort estimation (SDEE) plays a primary role in software project management. But choosing the appropriate SDEE technique remains elusive for many project managers and researchers. Moreover, the choice of a reliable estimation accuracy measure is crucial because SDEE techniques behave differently given different accuracy measures. The most widely used accuracy measures in SDEE are those based on magnitude of relative error (MRE) such as mean/median MRE (MMRE/MedMRE) and prediction at level p (Pred(p)), which counts the number of observations where an SDEE technique gave MREs lower than p. However, MRE has proven to be an unreliable accuracy measure, favoring SDEE techniques that underestimate. Consequently, an unbiased measure called standardized accuracy (SA) has been proposed. This paper deals with the Pred(p) and SA measures. We investigate (1) the consistency of Pred(p) and SA as accuracy measures and SDEE technique selectors, and (2) the relationship between Pred(p) and SA. The results suggest that Pred(p) is less biased towards underestimates and generally selects the same best technique as SA. Moreover, SA and Pred(p) measure different aspects of technique performance, and SA may be used as a predictor of Pred(p) by means of the 3 association rules.

Abstract Missing data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort estimation (SDEE) techniques. To deal with this challenge, several imputation techniques have been investigated in SDEE and k‐nearest neighbors (KNN)‐based imputation is still the most frequently used. To the best of our knowledge, no study has used support vector regression (SVR)‐based imputation to construct accurate estimation techniques, in particular those based on analogy. This paper introduces a new imputation technique based on SVR for handling MD in two analogy‐based SDEE techniques: classical analogy and fuzzy analogy. More specifically, we investigate whether the use of SVR instead of KNN in imputing MD improves the predictive performance of these two analogy‐based techniques. A total of 1134 experiments were conducted involving seven datasets, SVR/KNN MD imputation techniques (KNN with Euclidean and Manhattan distances), three missingness mechanisms (missing completely at random, missing at random, non‐ignorable missing), and MD percentages from 10% to 90%. The results suggest that the use of SVR imputation, rather than KNN imputation, may improve the prediction performance of both analogy‐based techniques. Furthermore, we found that the impact of MD percentage upon effort prediction performance is reduced when using SVR rather than KNN. Moreover, fuzzy analogy generates better estimates in terms of the standardized accuracy measure than classical analogy regardless of the MD technique, the dataset used, the missingness mechanism, or the MD percentage.

Software systems have established themselves as the heart of business and everyday living, and as the pillar of the emerging global digital economy. This puts pressure on educational institutions to train people for the continuously evolving software industry, which puts additional demand for new PhDs and educators. While large universities and research centres have the required expertise and infrastructure to providing a cost-effective training by research as well as covering wide spectrum of software engineering topics, the situation in small universities with limited resources is challenging. This is even more difficult for some countries where the discipline of software engineering is totally new, which is the case of emerging countries. This paper describes the Pathways to PhDs project funded by the European Commission. The long-term aim is to support the development, modernization and international visibility and excellence of higher education, namely education by research at the PhD level in Europe, while helping partner countries to develop new PhD programs and consolidate existing ones in the field of computing in the area of software engineering. This paper presents the creation of a common educational framework that consist of seven specialized short-intensive schools, each of them feature the expertise and "savoir-faire" of participating universities. The collaboration and communication among partners and the schools is supported by an innovative communication platform, which is also presented in the paper.

A certification exam is a test of knowledge on a specific subject to ensure qualification to perform a certain job or task. For the COSMIC organization, the scope of the certification exam includes the set of rules and procedures for measuring the functional size of software. The first exam was held in April 2006 and, since then, hundreds of participants from various countries have become COSMIC-certified. The motivation for the analysis reported here was to identify and investigate certification questions causing difficulties for examinees. The analysis presented is based on versions 3.1 and 4.0.1 of the COSMIC Measurement Manual and on the certification exams conducted in 2016. The data collected allows identification, for training purposes, of variations in the levels of understanding of the distinct sections of the COSMIC Measurement Manual or, if the source of wrong answers were not examinee-dependent, identification of questions needing improvements in wording.

Ensemble Effort Estimation (EEE) consists on predicting the software development effort by combining more than one single estimation technique. EEE has recently been investigated in software development effort estimation (SDEE) in order to improve the estimation accuracy. The overall results suggested that the EEE yield better prediction accuracy than single techniques. On the other hand, feature selection (FS) methods have been used in the area of SDEE for the purpose of reducing the dimensionality of a dataset size by eliminating the irrelevant and redundant features. Thus, the SDEE techniques are trained on a dataset with relevant features which can lead to improving the accuracy of their estimations. This paper aims at investigating the impact of two Filter feature selection methods: Correlation based Feature Selection (CFS) and RReliefF on the estimation accuracy of Heterogeneous (HT) ensembles. Four machine learning techniques (K-Nearest Neighbor, Support Vector Regression, Multilayer Perceptron and Decision Trees) were used as base techniques for the HT ensembles of this study. We evaluate the accuracy of these HT ensembles when their base techniques were trained on datasets preprocessed by the two feature selection methods. The HT ensembles use three combination rules: average, median, and inverse ranked weighted mean. The evaluation was carried out by means of eight unbiased accuracy measures through the leave-one-out-cross validation (LOOCV) technique over six datasets. The overall results suggest that all the attributes of most datasets used are relevant for building an accurate predictive technique since the ensembles constructed without features selection outperformed in general the ones using features selection. As for the combination rule, the median generally produces better results than the other two used in this empirical study.

Reorganizational projects in general and software-related projects in particular, are often implemented with a focus only on the reorganized components within an organizational management system, not taking into account relationships with the other components of an enterprise architecture (EA). This paper first looks at the current state of EA measurement to identify weaknesses and gaps in aligning and measuring EA components, EA structures and EA interrelationships from an IT perspective. It then identifies from related works available innovative measurement concepts that could contribute for aligning, measuring and monitoring software-related projects within an EA strategy. This includes measurement avenues within a Balanced Scorecard (BSC), contributions of functional size measurement to the BSC, and measurement of software structures and functionality within a service-oriented architecture (SOA).

Functional size measurement (FSM), a powerful tool for managing software projects, provides an objective and quantitative base for management decisions. In this paper, we discuss FSM for Internet of things (IoT) real-time embedded software (RTES), and propose a guideline using Arduino (a DIY - Do It Yourself - microcontroller widely used especially in the IoT domain) as a measurement example. As an illustration of the potential benefit of using FSM in the context of IoT, the use of functional size ranges is proposed for managing energy consumption of IoT RTES.

Concepts such as price, performance, time to completion (availability), probability of failure and nonrepudiation are key to developing a comparison service in order to establish Service Level Agreements (SLA) or design better mechanisms to improve performance in Cloud Computing Systems (CCS). This work presents our design of a performance analysis platform that includes an infrastructure, a framework for performance measurement and tools to facilitate the design, validation, and comparison of performance models and algorithms for CCS. The purpose of the platform is to help establish attribute-performance relationships relating to specific applications with relatively well-known demands on systems in order to be able to determine how comparison services may be formulated.

Ensemble methods have been used recently for prediction in data mining area in order to overcome the weaknesses of single estimation techniques. This approach consists on combining more than one single technique to predict a dependent variable and has attracted the attention of the software development effort estimation (SDEE) community. An ensemble effort estimation (EEE) technique combines several existing single/classical models. In this study, a systematic mapping study was carried out to identify the papers based on EEE techniques published in the period 2000-2015 and classified them according to five classification criteria: research type, research approach, EEE type, single models used to construct EEE techniques, and rule used the combine single estimates into an EEE technique. Publication channels and trends were also identified. Within the 16 studies selected, homogeneous EEE techniques were the most investigated. Furthermore, the machine learning single models were the most frequently employed to construct EEE techniques and two types of combiner (linear and non-linear) have been used to get the prediction value of an ensemble.

Over the last decades, the software engineering community has investigated new techniques for software development effort estimation. Unfortunately, the estimates were not always accurate. Error approaches are then, aninteresting track for improving the projects running performances and their financial profitability. The aim of this systematic mapping study is to summarize and synthesize theexisting studies dealing with effort estimation error and uncertainty and to classify them based on research approaches, contribution types, accuracy criteria, datasets, error approaches and effort estimation techniques used. In total 19papers published between 1990 and 2015 were selected. We observed a balance between the managerial approaches and the technical ones. Furthermore, the proposed errortechniques and frameworks improve in general the accuracy ofeffort estimation techniques. Fuzzy logic, bootstrapping andrisk analysis are promising avenues that could be combined with various estimation techniques.

A common challenge in life is to evaluate and deal with risks. Even though Risk management is fundamental to any activity, it is too often evaluated and managed from a qualitative rather than a quantitative perspective. In order to improve, too often organizations are seeking compliance against a single model/approach, forgetting that most often 'one model doesn't fit all' and that the target process model is the organizational one, strengthened by external best practices. An approach to process improvement that takes this into consideration is LEGO (Living EnGineering prOcess). LEGO extracts the most useful Elements of Interest (EoI) from several types of maturity models into an organizational Business Process Model (BPM) in order to facilitate to the achievement of higher organizational maturity and capability levels, that's the definitive intended target to be improved. This paper applies the LEGO approach to Risk Management, analyzing several Risk Management Maturity Models and unifying their practices in order to come up with a more comprehensive process model on risk management integrating multiple views.

Background: Modern software development companies increasingly rely on quantitative data in their decision-making for product releases, organizational performance assessment and monitoring of product quality. KPIs (Key Performance Indicators) are a critical element in the transformation of raw data (numbers) into decisions (indicators). The goal of the paper is to develop, document and evaluate a quality model for KPIs - addressing the research question of What characterizes a good KPI? In this paper we consider a KPI to be "good" when it is actionable and supports the organization in achieving its strategic goals. We use an action research collaborative project with an infrastructure provider company and an automotive OEM to develop and evaluate the model. We analyze a set of KPIs used at both companies and verify whether the organization's perception of these evaluated KPIs is aligned with the KPI's assessment according to our model. The results show that the model organizes good practices of KPI development and that it is easily used by the stakeholders to improve the quality of the KPIs or reduce the number of the KPIs. Using the KPI quality model provides the possibility to increase the effect of the KPIs in the organization and decreases the risk of wasting resources for collecting KPI data which cannot be used in practice.

The main purpose of this paper is to describe the design objectives that were set when work started on developing the COSMIC method of measuring a functional size of software requirements, the problems that were tackled during the development and some measurement results that demonstrate that the method has met its objectives. The paper begins by setting two main general objectives for software measurement, namely to help control software activities and to estimate future activities. Various approaches to software size measurement are described and the reasons are given why the COSMIC method was developed. The method is briefly described, followed by an account of advances by the COSMIC community. The paper concludes by discussing the main technical challenges that must still be tackled (principally measurement automation) and the organizational challenges (principally gaining a wider acceptance of the benefits of software measurement).

Within a software architecture design (SAD) project, designers deal with software design artifacts (SDAs) such as scenarios, patterns, and tactics.Each SDA has its unique issues and related architectural knowledge (AK) that may threaten the success of a project.This paper introduces the Software Architecture Mapping (SAM) framework to manage AK and associated issues by using finer-grained SDAs and networks of weighted arguments.These networks of data may be used to produce quantitative information in multi-dimensional views to facilitate the identification of critical SDAs and issues in a project.This paper illustrates how the SAM framework has been used to manage AK related to the template method (TM) design pattern in the context of an academic case study.

Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: (1) classical analogy-based technique and (2) 2FA-kmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are quite diverse and have different sizes: ISBSG, COCOMO, USP05-FT, and USP05-RQ. The results obtained showed that both 2FA-kprototypes and 2FA-kmodes perform better than classical analogy.

Software designers have to deal with a large number of distinct software design artifacts (SDAs), including requirements, patterns, and tactics.This paper proposes a technique that systematizes the classification of SDAs, and a classification scheme (CS) which organizes the SDAs into a matrix, in a manner derived from the Zachman Framework for enterprise architecture.An instantiation of this CS is a traceability matrix called a software-structure map (SSM) that records the SDAs and their relationships.The approach is illustrated through the analysis of the Template Method (TM) design pattern as an example of a SDA.

Missing Values (MV) present a serious problem facing research in software engineering (SE) which is mainly based on statistical and/or data mining analysis of SE data. The simple method of dealing with MV is to ignore data with missing observations. This leads to losing valuable information and then obtaining biased results. Therefore, various techniques have been developed to deal adequately with MV, especially those based on imputation methods. In this paper, a systematic mapping study was carried out to summarize the existing techniques dealing with MV in SE datasets and to classify the selected studies according to six classification criteria: research type, research approach, MV technique, MV type, data types and MV objective. Publication channels and trends were also identified. As results, 35 papers concerning MV treatments of SE data were selected. This study shows an increasing interest in machine learning (ML) techniques especially the K-nearest neighbor algorithm (KNN) to deal with MV in SE datasets and found that most of the MV techniques are used to serve software development effort estimation techniques.

Analogy-based estimation has recently emerged as a promising technique and a viable alternative to other conventional estimation methods. One of the most important research areas for analogy-based cost estimation is how to predict the effort of software projects when they are described by mixed numerical and categorical data. To address this issue, we have proposed, in an earlier work, a new approach called fuzzy analogy combining the key features of fuzzy logic and analogy-based reasoning. However, fuzzy analogy may only be used when the possible values of the categorical attributes are derived from a numerical domain. The current study aims to extend our former approach to correctly handle categorical data. To this end, the fuzzy k-modes algorithm is used with two initialization techniques. The performance of the proposed approach was compared with that of classical analogy using the International Software Benchmarking Standards Group (ISBSG) dataset. The obtained results show significant improvement in estimation accuracy.

Cloud Computing is an emerging technology for processing and storing very large amounts of data. Sometimes, anomalies and defects affect a part of the Cloud infrastructure, resulting in degradation in Cloud performance. This work uses the Taguchi method for the design of experiments to present a methodology for identifying the relationships between the various configuration parameters that affect the quality of Cloud Computing applications performance. This paper is based on a proposed performance measurement framework for Cloud Computing systems, which integrates software quality concepts from ISO 25010 and other international standards.

In the Dark Ages the 'Lords of the country' were expecting that their 'alchemists' - the 'gurus' of their era - would come up with mysterious formula that would transform 'dust' into 'pots of gold! A lot of current software estimation models and practices, including in Agile and COCOMO-like models, share characteristics of craft practices observed in the Dark Ages, when compared to current professional practices from engineering and sciences.

Analogy-based software development effort estimation methods have proved to be a viable alternative to other conventional estimation methods since they mimic the human problem solving approach. However, they are limited by their inability to correctly handle categorical data. Therefore, we have proposed, in an earlier work, a new approach called fuzzy analogy which extends classical analogy by incorporating the fuzzy logic concept in the estimation process. The proposed approach may be applied only when the categorical values are derived from numerical data. This paper extends fuzzy analogy to deal with categorical values that are not derived from numerical data. To this aim, we used the fuzzy k-modes algorithm, a well-known clustering technique for large datasets containing categorical values. Thereafter, we evaluate the accuracy of fuzzy analogy construction-based on fuzzy k-modes using the ISBSG R8 dataset. This evaluation shows that our proposed approach leads to significant improvement in estimation accuracy.

Over the past few years ISO has published a number of specific standards detailing processes included in a generic form in software development life cycle models. ISO 15939 on the Measurement process itself is an example of such specific ISO standard. This paper presents some suggestions for improvements to its Measurement Information Model and to the measurement plan within the planning process of ISO 15939.

Automating functional size measurement (FSM) is important for organizations needing to measure a large number of projects within a short timeframe, provided, of course, that the results automatically generated are accurate, particularly when such measurement is based on an international measurement standard. A literature review has shown that very little work has been conducted on verifying measurement results produced by FSM automation. This paper presents a verification protocol designed to provide evidence of the accuracy of an automated FSM tool using the COSMIC ISO 19761 measurement standard. Also included are: an example of its use for the verification of an AUTOSAR-based FSM automation prototype tool developed at ESTACA, and a case study on the application of the proposed verification protocol on another prototype tool developed at Renault S.A.

In software engineering, the standards for functional size measurement require that the functionality to be measured be fully known for accurate measurement results. Therefore, in the early phases of software development, when there is a lack of detail, approximate sizing approaches must be used instead of the standards themselves. Approximate sizing techniques are typically based on the analysis of historical data of the functional size of a number of completed projects within an organization. For organizations without historical data on the functional size of their completed projects, this paper proposes a fuzzy logic model to approximate functional size early in the software development process. It also reports on a quantitative case study of this approximation technique from industry, and compares its performance with the Equal Size Band approximation approach.

The purpose of this workshop is to provide an opportunity for practitioners of the COSMIC method and newcomers to learn about the latest developments and thinking in the evolution of the COSMIC method. Each topic will be introduced via a presentation with an invitation to ask questions at any stage.

Software effort estimation is one of the most important tasks in software project management. Of several techniques suggested for estimating software development effort, the analogy-based reasoning, or Case-Based Reasoning (CBR), approaches stand out as promising techniques. In this paper, the benefits of using linguistic rather than numerical values in the analogy process for software effort estimation are investigated. The performance, in terms of accuracy and tolerance of imprecision, of two analogy-based software effort estimation models (Classical Analogy and Fuzzy Analogy, which use numerical and linguistic values respectively to describe software projects) is compared. Three research questions related to the performance of these two models are discussed and answered. This study uses the International Software Benchmarking Standards Group (ISBSG) dataset and confirms the usefulness of using linguistic instead of numerical values in analogy-based software effort estimation models.

The use of functional size measurement (FSM) methods in software development organizations is growing during the years. Also, object oriented (OO) techniques have become quite a standard to design the software and, in particular, Use Cases is one of the most used techniques to specify functional requirements. Main FSM methods do not include specific rules to measure the software functionality from its Use Cases analysis. To deal with this issue some other methods like Kramer’s functional measurement method have been developed. Therefore, one of the main issues for those organizations willing to use OO functional measurement method in order to facilitate the use cases count procedure is how to convert their portfolio functional size from the previously adopted FSM method towards the new method. The objective of this research is to find a statistical relationship for converting the software functional size units measured by the International Function Point Users Group (IFPUG) function point analysis (FPA) method into Kramer-Smith’s use cases points (UCP) method and vice versa. Methodologies for a correct data gathering are proposed and results obtained are analyzed to draw the linear and non-linear equations for this correlation. Finally, a conversion factor and corresponding conversion intervals are given to establish the statistical relationship.

The capabilities and resources offered by mobile technologies are still far from those provided by fixed environments, and this poses serious challenges, in terms of evaluating the quality of applications operating in mobile environments. This article presents a study to help quality managers apply the ISO 9126 standard on software quality, particularly the External Quality model, to mobile environments. The influence of the limitations of mobile technologies are evaluated for each software quality characteristic, based on the coverage rates of its external metrics, which are themselves influenced by these limitations. The degrees of this influence are discussed and aggregated to provide useful recommendations to quality managers for their evaluation of quality characteristics in mobile environments. These recommendations are intended for mobile software in general and aren't targeted a specific ones. The External Quality model is especially valuable for assessing the Reliability, Usability, and Efficiency characteristics, and illustrates very well the conclusive nature of the recommendations of this study. However, more study is needed on the other quality characteristics, in order to determine the relevance of evaluating them in mobile environments.

This paper proposes a measurement method to measure the non-functional portability software requirement. The proposed method complies with the COSMIC ISO 19761 measurement method standard. The details of the design steps are presented and explained. This paper also includes an analysis and possible utilizations of the method results into the various estimation models. Applying the proposed method can be beneficial to estimate the development time and/or the quality assurance efforts to develop/certify portable software on the various environments supported.

Although the Agile Software Development (ADS) approach has been around for the last 15 years, it is only recently that attention has moved towards Agile Software Management (ASM) for tackling some of the management-related weaknesses, such as estimating on the basis of User Story points. This paper presents an application of the INVEST criteria (Independent - Negotiable - Valuable - Estimable - Small -Testable) for improving the measurement technique of User Stories, introducing sizing units and a technique to negotiate requirements. It includes a discussion on an approach to balancing the six criteria used to evaluate a set of User Stories in a Sprint.

The two ongoing repositories of software projects in the software engineering community are the ISBSG (International Software Benchmarking Standards Group) Repository and PROMISE (Predictor Models In Software Engineering). These repositories lack structured documentation and a researcher interested in using the datasets has to conduct his own investigation to identify the datasets that are suitable for his purposes. This paper provides additional information on these datasets by identifying the topics addressed, highlighting the availability of the data file and of the description of attributes related to the datasets, and indicating their usefulness for benchmarking studies.

Mobile applications are gaining in popularity because of the significant advantages of mobile devices, such as portability, location awareness, electronic identity, and an integrated camera. However, these devices have a number of disadvantages in terms of usability, like limited resources and small screen size. Evaluating the usability of applications developed for mobile operating systems is a very important step in addressing these disadvantages and achieving success in mobile application markets, such as Apple's App Store. Usability evaluation must be tailored to all the various mobile operating systems in use, as they each have their own particular characteristics. This paper presents a mobile application usability evaluation framework for one of the most popular mobile operating systems, iOS. A set of questions is defined and applied to evaluate the usability of eleven applications available at the App Store.

When reusable software elements are defined in the requirements phase, this raises a number of issues for the measurement of their functional size, when a reusable element implements a business rule or a business sub process, for example. A case study is presented with project functional size measurement data to illustrate the impact of reuse on the resulting size and to determine criteria of reuse effectiveness and efficiency.

Even though software engineering education embraces the teaching of software measurement topics, there is a lack of guidelines to address this teaching in undergraduate programs. In order to facilitate the education of these topics at the undergraduate level, a set of studies was conducted since 2010 and an educational framework is being developed based on the opinions of experts in the field. The framework provides guidelines for university teachers and instructors to facilitate the teaching and learning process of software measurement for undergraduate students or beginners in the field. The structure of this framework can be applied in other educational fields.

SUMMARY The nonfunctional requirements (NFR) are often captured only generically at a fairly high level, and they do not include the levels of detail necessary at this stage for the system engineers to allocate them as specific functionalities to be handled either by the software or the hardware, or a specific combination of the two. The European Cooperation for Space Standardization (ECSS) series of standards for the aerospace industry includes maintainability requirements as one of 16 types of NFR for embedded and real‐time software. A number of maintainability‐related concepts are dispersed throughout the ECSS, ISO 9126, and Institute of Electrical and Electronics Engineers standards to describe, at varying levels of detail, the various types of candidate maintainability requirements at the system, software, and hardware levels. This paper organizes these dispersed maintainability concepts into a standards‐based reference model of system maintainability requirements. The availability of this reference model can facilitate the early identification of the system maintainability‐NFR and their detailed allocation as specific maintainability functions to be handled by the specified allocation to hardware or software, or a specific combination of the two. In the absence of such a reference model, these NFR are typically handled in practice much later on in the software development life cycle, when at system testing time, users and developers find out that a number of maintainability requirements have been overlooked and additional effort has to be expended to implement them. The approach adopted in this research for the structure of this reference NFR model is based on the generic model of software functional requirements proposed in the COSMIC – ISO 19761 model, thereby allowing the functional size of such maintainability requirements allocated to software to be measured. Copyright © 2012 John Wiley & Sons, Ltd.

Models are key artifacts in Model-Driven Development (MDD) methods. To produce high-quality software by using MDD methods, quality assurance of models is of paramount importance. To evaluate the quality of models, defect detection is considered a suitable approach and is usually applied using reading techniques. However, these reading techniques have limitations and constraints, and new techniques are required to improve the efficiency at finding as many defects as possible. This article presents a case study that has been carried out to evaluate the use of a Functional Size Measurement (FSM) procedure in the detection of defects in models of an MDD environment. To do this, we compare the defects and the defect types found by an inspection group with the defects and the defect types found by the FSM procedure. The results indicate that the FSM is useful since it finds all the defects related to a specific defect type, it finds different defect types than an inspection group, and it finds defects related to the correctness and the consistency of the models.

The data repository of the International Software Benchmarking Standards Group (ISBSG) can be used by researchers to investigate cause and effect relationships by enabling them to study which variables contribute to achieving certain objectives, such as increasing productivity and improving quality. The ISO 9126 series proposes a number of software quality models, and a large inventory of candidate derived measures for those models. However, even if in the 25000 series (an upcoming version of ISO 9126) there is a plan to better define the base and derived measures, neither the ISO 9126 nor ISO 25000 series include a data repository or intend to create one, and without data for comparison purposes or for analyzing actual relationships across quality attributes and models, the series is challenging to use in practice. This paper concurrently analyzes ISO 9126 and the ISBSG data repository on software projects in order to identify the subset of ISO 9126 quality characteristics that is referenced in the ISBSG repository. It also identifies a number of quality-related data fields from the ISBSG which can be useful in empirical and benchmarking studies.

Business process models have proved to be useful for requirements elicitation. Since software development depends on the quality of the requirements specifications, generating high-quality business process models is therefore critical. A key factor for achieving this is the expressiveness in terms of completeness and clarity of the modeling notation for the domain being modeled. The Bunge-Wand-Weber (BWW) representation model is frequently used for assessing the expressiveness of business process modeling notations. This article presents some propositions to adapt the BWW representation model to allow its application to the software requirements elicitation domain. These propositions are based on the analysis of the Guide to the Software Engineering Body of Knowledge (SWEBOK) and the Guide to the Business Analysis Body of Knowledge (BABOK). The propositions are validated next by experts in business process modeling and software requirements elicitation. The results show that the BWW representation model requires to be specialized by including concepts specific to software requirements elicitation.

This paper presents a quantitative management framework for continuous improvement initiatives to ensure the sustainability of earnings and value in software intensive organizations (SIO). Three groups of measures are proposed to monitor the impact of changes introduced by continuous improvement initiatives: a network of dashboards, a performance measurement system (PMS) and a strategy management system (SMS). The proposed framework was experimented in two small organizations with tailored quantitative models and tools to support decision making at all levels of management.

COSMIC-based functional size measurement (FSM) procedures should all produce the same measurement results, no matter what design decisions are made when modeling the functional requirements of the software to be measured. When the FSM procedure proposed in [14] is applied to different models of the same functional requirements, the measurement results vary. This problem is addressed, and a refined FSM procedure for real-time embedded software is proposed. The revised rules of this refined FSM procedure are based on an analysis of the issues detected in [14], and a solution to the variance issue is offered as a result.

In mature engineering disciplines, international consensus can be reached on measurement, as evidenced through established measurement standards. In software engineering, there are 5 functional size measurement standards. These standards work best when the functionality to be measured is fully known, although this usually doesn't happen in the early phases of software development. The techniques most often used to approximate the sizing of the software to be developed in the early phases involve historical data. However, gathering historical data is a challenge in itself. This paper proposes the use of a fuzzy logic model to approximate the functional size of a piece of software.

Expert-based effort prediction in software projects can be taught, beginning with the practices learned in an academic environment in courses designed to encourage them. However, the length of such courses is a major concern for both industry and academia. Industry has to work without its employees while they are taking such a course, and academic institutions find it hard to fit the course into an already tight schedule. In this research, the set of Personal Software Process (PSP) practices is reordered and the practices are distributed among fewer assignments, in an attempt to address these concerns. This study involved 148 practitioners taking graduate courses who developed 1,036 software course assignments. The hypothesis on which it is based is the following: When the activities in the original PSP set are reordered into fewer assignments, the result is expert-based effort prediction that is statistically significantly better.

SUMMARY Measurement based on the international standards for measurement (i.e., metrology) is not the same as judgmental‐based quantification of implicit relationships across a mix of entities and attributes without due consideration of admissible mathematical operations on numbers of different scale types. This paper presents first the Measurement Information Model in ISO 15939 and clarifies next what in it refers to the classical metrology field, and what refers to the quantitative analysis of relationships. The paper concludes with two examples of the designs of a measure for ISO 9126, one focusing on a single attribute and the second attempting to quantify a set of relationships across a number of entities and attributes. Copyright © 2010 John Wiley & Sons, Ltd.

Agile Project Management (APM) must adopt dynamic project plans to better handle the uncertainty and unpredictability associated with the Agile Software Development (ASD) methodologies. For these dynamic project plans, the functional size of the evolving requirements can be measured with COSMIC measurement method. To support this measurement activity, the quality of the documentation of the user stories, which represent the user requirements, is important to be interpreted correctly. In the research reported here, the COSMIC method is used to analyze and report on the quality of the documentation of user stories.

To obtain the functional size of software and reduce measurement variance caused by the interpretations of individual measurers, a number of measurement procedureshave been designed based on measurement methods approved as international standards. To date, most of these procedures have targeted Management Information Systems software, while only a few were designed for real-time and reactiveembedded software. In this paper, we propose a functional size measurement (FSM) procedure for real-time embedded software, based on the COSMIC method, version 3.0.1 (ISO 19761), and with requirements documented using the Simulinkmodel. The design of this FSM procedure is based on the mapping of key concepts in both Simulink and COSMIC, and the identification of the mapping rules for extracting the information stored in the Simulink files that is required formeasurement. This procedure therefore provides the foundation for automating the measurement of software requirements documented using Simulink. The backgroundstudy for this procedure was conducted at Renault SAS using ECU (Electronic Control Unit) functional requirements expressed with the Simulink tool.

This paper reports on a research project investigating the contribution of functional size measurers to finding defects in requirements. In previous experiments, the concurrent inspection of the same requirements document by a measurer in addition to inspectors showed that the functional size measurement activity adds value in finding functional defects that inspectors have not identified. This paper presents an analysis of the influence of defects on the functional size once many of the identified defects have been fixed. For these experiments, the measurers used COSMIC - ISO 19761 to measure functional size and to find defects. Experiments findings show an increase in functional size and a decrease in defects identified.

In mature engineering disciplines and science, mathematics and measurement are considered as important subjects to be taught in university programs. This paper discusses about these subjects in terms of their respective meanings and complementarities. It also presents a discussion regarding their maturity, relevance and innovations in their teaching in engineering programs. This paper pays special attention to the teaching of software measurement in higher education, in particular with respect to mathematics and measurement in engineering in general. The findings from this analysis will be useful for researchers and educators interested in the enhancement of educational issues related to software measurement.

ISO 14143-1 specifies that a functional size measurement (FSM) method must provide measurement procedures to quantify the functional user requirements (FURs) of software. Such quantitative information, functional size, is typically used, for instance, in software estimation. One of the international standards for FSM is the COSMIC FSM method — ISO 19761 — which was designed to be applied both to the business application (BA) software domain and to the real-time software domain. A recurrent problem in FSM is the availability and quality of the inputs required for measurement purposes; that is, well documented FURs. Business process (BP) models, as they are commonly used to gather requirements from the early stages of a project, could be a valuable source of information for FSM. In a previous article, the feasibility of such an approach for the BA domain was analyzed using the Qualigram BP modeling notation. This paper complements that work by: (1) analyzing the use of BPMN for FSM in the BA domain; (2) presenting notation-independent guidelines for the BA domain; and (3) analyzing the possibility of using BP models to perform FSM in the real-time domain. The measurement results obtained from BP models are compared with those of previous FSM case studies.

Models are key artifacts in Model-Driven Development (MDD) methods. To evaluate the quality of models, defect detection is considered to be a suitable approach, which is usually applied using reading techniques. However, new techniques are required in order to find as many defects as possible. This paper presents a case study to evaluate the usefulness of a Functional Size Measurement (FSM) procedure to detect defects in models of a MDD environment. The results indicate that the FSM is useful in finding all the defects that are related to a defect type as well as finding different defect types than an inspection team does.

The European ECSS-E-40 series of standards for the aerospace industry includes interfaces as one of 16 types of non functional requirement (NFR) for embedded and real-time software. An interface is typically described at the system level as a non functional requirement, and a number of concepts and terms are provided in that series to describe various types of candidate interfaces. This paper collects and organizes these interface-related descriptions into a generic model for the specification of software interface requirements, and to measure their functional size for estimation purposes using the COSMIC ISO 19761 standard.

Evidence shows that proposals for new modeling notations emerge and evolution of current ones are becoming more complex, often in an attempt to satisfy the many different modeling perspectives required by each stakeholder. This paper presents a method to identify the specific notation construct requirements, at multiple levels of abstraction, which satisfy the needs of a stakeholder when performing a specific task. Initially the focus is on two different stakeholders: software engineers (SE) and business analysts(BA), and one specific software engineering activity: requirements eliciting and analysis. The specific body of knowledge of the two stakeholders (Software Engineering Book of Knowledge (SWEBOK) for the SE, and Business Analysis Body of Knowledge (BABOK) for the BA) are used to identify each stakeholder specific notation construct requirements, at multiple levels of abstraction, in order to propose a simplification of their notation and constructs set. This paper presents solution avenues to simplify business process modeling notations by identifying the specific constructs preferred by different stakeholders.

For software organizations needing ISO 9001 certification, it is important to establish a software process life cycle that can manage the requirements imposed by this certification standard. This paper presents an analysis of extreme programming (XP) from the ISO 9001 and ISO 90003 perspectives. The focus is to extract the requirements related to the ISO product realization process and to determine the strengths and weaknesses of XP in handling those requirements.

This paper presents an overview of quantitative analysis techniques for software quality and their applicability during the software development life cycle (SDLC). This includes the Seven Basic Tools of Quality, Statistical Process Control, and Six Sigma, and it highlights how these techniques can be used for managing and controlling the quality of software during specification, design, implementation, testing, and maintenance. We verify whether or not these techniques, which are generally accepted for most projects, most of the time, and have value that is recognized by the peer community, have indeed been included in the SWEBOK Guide.

This paper reports on a research project investigating the contribution of functional size measurers to finding defects in requirements. It describes an experiment where the same requirements document was inspected by a number of inspectors, as well as by a number of measurers. All participants had limited experience in both inspecting and measuring. The number and types of defects found by the two groups are compared and discussed. For this experiment, the measurers used COSMIC - ISO 19761 to measure functional size and to find defects. Results show an increase in defect identification when both inspection and functional size measurement are used to find and report defects.

Software component technology has a substantial impact on modern IT evolution. The benefits of this technology, such as reusability, complexity management, time and effort reduction, and increased productivity, have been key drivers of its adoption by industry. One of the main issues in building component-based systems is the reliability of the composed functionality of the assembled components. This paper proposes a reliability assessment model based on the architectural configuration of a component-based system and the reliability of the individual components, which is usage- or testing-independent. The goal of this research is to improve the reliability assessment process for large software component-based systems over time, and to compare alternative component-based system design solutions prior to implementation. The novelty of the proposed reliability assessment model lies in the evaluation of the component reliability from its behavior specifications, and of the system reliability from its topology; the reliability assessment is performed in the context of the implementation-independent ISO/IEC 19761:2003 International Standard on the COSMIC method chosen to provide the component's behavior specifications. In essence, each component of the system is modeled by a discrete time Markov chain behavior based on its behavior specifications with extended-state machines. Then, a probabilistic analysis by means of Markov chains is performed to analyze any uncertainty in the component's behavior. Our hypothesis states that the less uncertainty there is in the component's behavior, the greater the reliability of the component. The system reliability assessment is derived from a typical component-based system architecture with composite reliability structures, which may include the composition of the serial reliability structures, the parallel reliability structures and the p-out-of-n reliability structures. The approach of assessing component-based system reliability in the COSMIC context is illustrated with the railroad crossing case study.

Software component technology has become a major pillar of the IT evolution. The benefits of this technology, such as reuse, enhanced quality and relatively short application development time, have been key drivers of its industrial adoption. However, in its progress towards maturity, component technology has suffered from a number of limitations, such as unused component members (data and functionalities). For instance, a reusable software component incorporates a set of members, a sizevarying subset of which is actually used to satisfy the functional requirements of a particular software application. This means that a complementary subset of unused members will persist in the deployed application, where this subset provides no functional value to the host application. Furthermore, these unused members can consume memory and network resources and might compromise application integrity and/or security if they are exploited inappropriately. In this paper, we propose CoMet, a prototype tool which applies CUMM (Component Unused Member Measurement) method to measure unused component members (attributes and functionalities) and their usage percentages in a software application.

We address the assessment and improvement of the software maintenance function by proposing a maturity model for daily software maintenance activities: the Software Maintenance Maturity Model (SMmm). The software maintenance function suffers from a scarcity of management models to facilitate its evaluation, management, and continuous improvement. The SMmm addresses the unique activities of software maintenance while preserving a structure similar to that of the Capability Maturity Model integration (CMMi). It is designed to be used as a complement to that model. The SMmm is based on practitioners' experience, international standards, and the seminal literature on software maintenance. We present the model's purpose, scope, foundation, and architecture, followed by its initial validation. Copyright © 2005 John Wiley & Sons, Ltd.

We propose a measurement repository for collecting, storing, analyzing and reporting measurement data based on the requirements of the capability maturity model integrated (CMMI). Our repository is generic, flexible and integrated, supporting a dynamic measurement system. It was originally designed to support Ericsson Research Canada's business information needs. Our multidimensional repository can relate measurement information needs to CMMI processes and products requirements. The data model is based on a hierarchical and multidimensional definition of measurement data. It has been developed based on the concept of a data warehouse environment. Reporting features are based on the definition of queries to on line analytical process (OLAP) cubes. OLAP cubes are created as materialized views of the measurement data, and the user functionalities are implemented as analytical drill-down/roll-up capabilities and as indicator and trend analysis capabilities.

Software development effort estimation with the aid of neural networks has generally been viewed with skepticism by a majority of the software cost estimation community. Although, neural networks have shown their strengths in solving complex problems, their shortcoming of being 'black boxes' models has prevented them from being accepted as a common practice for cost estimation. In this paper, we study the interpretation of cost estimation models based on a backpropagation three layer perceptron network. Our proposed idea comprises mainly of the use of a method that maps this neural network to a fuzzy rule based system. Consequently, if the obtained fuzzy rules are easily interpreted, the neural network will also be easy to interpret. Our case study is based on the COCOMO'81 dataset.

Data manipulation, or algorithmic complexity, is not taken into account adequately in any of the most popular functional size measurement methods. In this paper, we recall some well-known methods for measuring problem complexity in data manipulation and highlight the interest in arriving at a new definition of complexity. Up to now, the concept of effort has always been associated with complexity. This definition has the advantage of dissociating effort and complexity, referring instead to the characteristics and intrinsic properties of the software itself. Our objective is to propose some simple and practical approaches to the measurement of some of these characteristics which we consider particularly relevant and to incorporate them into a functional size measurement method.

Abstract Even though a significant number of estimation models have been proposed for development projects, few have been proposed for software maintenance. This paper reports on two field studies carried out on the use of functional size measures in building estimation models for sets of maintenance projects implementing small functional enhancements in existing software. The first field‐study reports on models built with 15 projects making functional enhancements to an internet‐based software program for linguistic applications. The second field study analyses 19 maintenance projects on a single real‐time embedded software program in the defense industry. Both field studies collected functional size measures using version 2.0 of the COSMIC‐FFP functional size measurement method. Also both field studies classified projects into two classes of project difficulty in order to aid identifying subsets of projects with greater homogeneity in the relationship of project effort to functional size. This paper is the first published paper reporting on the use this second generation of functional size‐measurement methods in a maintenance‐estimation context. Copyright © 2002 John Wiley & Sons, Ltd.

During 1997, a large Information System (IS) Division of a Canadian phone company implemented formal process assurance in its Quality Assurance group. The status report presents a new perspective on the measurement of process assurance and the lessons learned after one year of assessing the individual conformance of software development projects to the corporate software development process (CSDP) of the organization. The status report presents the assurance process overview, goals. Benefits and scope, as well as the 1997 results overview, followed by the lessons learned for the 1998 audit program.

Function point analysis (FPA) was initially designed on the basis of expert judgments, without explicit reference to any theoretical foundation. From the point of view of the measurement scales used in its measurement process, FPA constitutes a potpourri of scales not admissible without the transformations imbedded in the implicit models of expert judgments. The results of this empirical study demonstrate that in a homogeneous environment not burdened with major differences in productivity factors there is a clear relationship between FPA's primary components and work-effort. This empirical study also indicates that there is such a relationship for each step of the FPA measurement process prior to the mixing of scales and the assignments of weights. Comparisons with FPA productivity models based on weights confirm, on the one hand, that the weights do not add information and, on the other, that the weights are fairly robust and can be used when little historical data is available. The full data set is provided for future studies.

A requirement for productivity models and productivity analysis is the ability to define a sizing technique of the product of a work process. There is currently a lack of measurement techniques for sizing the work product of software maintenance activities. This paper reports on research work carried out to define a sizing technique for the work product of the maintenance activities of the adaptive category. The proposed sizing technique is based on an extension to the Function Points technique which has been designed to measure the functional size of software applications from a user's perspective. However, it has been used mostly to measure either medium or large development or enhancement projects. An extended version of Function Points is proposed to take into account a finer level of granularity congruent with the small maintenance work products. This extension provides the ability to adequately size products that would have been previously bundled within the same size interval when using the conventional technique. This extension was field tested in an organization over a four-year period and preliminary results are discussed.

Abstract This paper is concerned with the identification and measurement of reuse within projects in which functional enhancements have been added to existing software applications. The proposed approach is based on the measurement of reuse from a functional perspective rather than from a technical perspective. Two key concepts are introduced: a reuse indicator and a predictor ratio. The reuse indicator is derived from an analysis of the function types as currently defined in Function Points Analysis. The predictor ratio is derived from an understanding of the avoided‐cost concept and of how it can be captured using historical databases of function points from previous development projects. This paper indicates how, in functional enhancement projects, the predictor ratio can be combined into the reuse indicator to derive an alternative size measure which takes into account functions reused and not redeveloped. The paper also demonstrates how these ratios can then be integrated in a maintenance productivity model to analyse the benefits of reuse by taking into account the avoided cost of functions reused. A case study based on an industrial data set is provided to illustrate the measurement of functional reuse in an enhancement project and its impact in maintenance productivity analysis.

The use of productivity models for enhancement projects is illustrated, and the reliability of function points-based models is reported. The results of a field study at a major Canadian financial institution indicate that function points-based productivity models are within the range of the recommended criteria for good models in software engineering: a mean relative error of /spl plusmn/25% in 75% of cases.>

Abstract While various figures have been published on the workload distribution of maintenance activities, this information is at best indicative of management perceptions, most of it originating from surveys, and almost none based on actual data. The reason for such a lack of published data is very simple: the industry does not measure or collect maintenance‐type data in a timely and accurate fashion. This article presents empirical data from a two‐year measurement effort in the maintenance environment of a Canadian financial institution. The findings reported here are based on a daily data‐collection process including 2152 work requests, which required 11 332 days to complete. Based on the supply/demand paradigm, maintenance data have been collected and analysed to investigate the basis of productivity analyses through such concepts as the product group, the product mix and the product mix changes on the demand side, as well as resource allocation by product classification and quarterly and yearly distribution changes. This paper includes a discussion on the measurement program implemented, and illustrates how insights into the maintenance process are gained through various measurements. The paper also presents hard data on the demand side and on the supply side of the maintenance process, as well as an analysis of the data collected. The findings reported are part of a larger measurement program introduced to manage the maintenance area in the same way as any other business unit.

Empirical data from a two-year measurement effort in the software maintenance environment of a Canadian organization are presented. The findings reported are based on a daily data-collection process including 2152 work requests which required 11365 days to complete. This set of empirical data confirms some of the findings of the opinion surveys in terms of the stability of the overall workload distribution in the maintenance area, while the analysis of any subset of data indicates that this overall average hides significant differences. The authors include a discussion on the improved measurement program implemented, and illustrate how insights into the maintenance process are gained through various measurements.>