Object-oriented software development effort prediction using design patterns from object interaction analysis

Software project management is arguably the most important activity in modern software development projects. In the absence of realistic and objective management, the software development process cannot be managed in an effective way. Software development effort estimation is one of the most challenging and researched problems in project management. With the advent of object-oriented development, there have been studies to transpose some of the existing effort estimation methodologies to the new development paradigm. However, there is not in existence a holistic approach to estimation that allows for the refinement of an initial estimate produced in the requirements gathering phase through to the design phase. A SysML point methodology is proposed that is based on a common, structured and comprehensive modeling language (OMG SysML) that factors in the models that correspond to the primary phases of object-oriented development into producing an effort estimate. This dissertation presents a Function Point-like approach, named Pattern Point, which was conceived to estimate the size of object-oriented products using the design patterns found in object interaction modeling from the late OO analysis phase. In particular, two measures are proposed (PP1 and PP2) that are theoretically validated showing that they satisfy wellknown properties necessary for size measures. An initial empirical validation is performed that is meant to assess the usefulness and effectiveness of the proposed measures in predicting the development effort of object-oriented systems. Moreover, a comparative analysis is carried out; taking into account several other size measures. The experimental results show that the Pattern Point measure can be effectively used during the OOA phase to predict the effort values with a high degree of confidence. The PP2 metric yielded the best results with an aggregate PRED (0.25) = 0.874

Towards making functional size measurement easily usable in practice

Functional Size Measurement methods –like the IFPUG Function Point Analysis and COSMIC methods– are widely used to quantify the size of applications. However, the measurement process is often too long or too expensive, or it requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified measurement methods have been proposed. This research explores easily usable functional size measurement method, aiming to improve efficiency, reduce difficulty and cost, and make functional size measurement widely adopted in practice. The first stage of the research involved the study of functional size measurement methods (in particular Function Point Analysis and COSMIC), simplified methods, and measurement based on measurement-oriented models. Then, we modeled a set of applications in a measurement-oriented way, and obtained UML models suitable for functional size measurement. From these UML models we derived both functional size measures and object-oriented measures. Using these measures it was possible to: 1) Evaluate existing simplified functional size measurement methods and derive our own simplified model. 2) Explore whether simplified method can be used in various stages of modeling and evaluate their accuracy. 3) Analyze the relationship between functional size measures and object oriented measures. In addition, the conversion between FPA and COSMIC was studied as an alternative simplified functional size measurement process. Our research revealed that: 1) In general it is possible to size software via simplified measurement processes with acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the details of both data and operations. The models obtained from out dataset yielded results that are similar to those reported in the literature. All simplified measurement methods that use predefined weights for all the transaction and data types identified in Function Point Analysis provided similar results, characterized by acceptable accuracy. On the contrary, methods that rely on just one of the elements that contribute to functional size tend to be quite inaccurate. In general, different methods showed different accuracy for Real-Time and non Real-Time applications. 2) It is possible to write progressively more detailed and complete UML models of user requirements that provide the data required by the simplified COSMIC methods. These models yield progressively more accurate measures of the modeled software. Initial measures are based on simple models and are obtained quickly and with little effort. As V models grow in completeness and detail, the measures increase their accuracy. Developers that use UML for requirements modeling can obtain early estimates of the applications‘ sizes at the beginning of the development process, when only very simple UML models have been built for the applications, and can obtain increasingly more accurate size estimates while the knowledge of the products increases and UML models are refined accordingly. 3) Both Function Point Analysis and COSMIC functional size measures appear correlated to object-oriented measures. In particular, associations with basic object- oriented measures were found: Function Points appear associated with the number of classes, the number of attributes and the number of methods; CFP appear associated with the number of attributes. This result suggests that even a very basic UML model, like a class diagram, can support size measures that appear equivalent to functional size measures (which are much harder to obtain). Actually, object-oriented measures can be obtained automatically from models, thus dramatically decreasing the measurement effort, in comparison with functional size measurement. In addition, we proposed conversion method between Function Points and COSMIC based on analytical criteria. Our research has expanded the knowledge on how to simplify the methods for measuring the functional size of the software, i.e., the measure of functional user requirements. Basides providing information immediately usable by developers, the researchalso presents examples of analysis that can be replicated by other researchers, to increase the reliability and generality of the results

Towards making functional size measurement easily usable in practice

Functional Size Measurement methods \u2013like the IFPUG Function Point Analysis and COSMIC methods\u2013 are widely used to quantify the size of applications. However, the measurement process is often too long or too expensive, or it requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified measurement methods have been proposed. This research explores easily usable functional size measurement method, aiming to improve efficiency, reduce difficulty and cost, and make functional size measurement widely adopted in practice. The first stage of the research involved the study of functional size measurement methods (in particular Function Point Analysis and COSMIC), simplified methods, and measurement based on measurement-oriented models. Then, we modeled a set of applications in a measurement-oriented way, and obtained UML models suitable for functional size measurement. From these UML models we derived both functional size measures and object-oriented measures. Using these measures it was possible to: 1) Evaluate existing simplified functional size measurement methods and derive our own simplified model. 2) Explore whether simplified method can be used in various stages of modeling and evaluate their accuracy. 3) Analyze the relationship between functional size measures and object oriented measures. In addition, the conversion between FPA and COSMIC was studied as an alternative simplified functional size measurement process. Our research revealed that: 1) In general it is possible to size software via simplified measurement processes with acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the details of both data and operations. The models obtained from out dataset yielded results that are similar to those reported in the literature. All simplified measurement methods that use predefined weights for all the transaction and data types identified in Function Point Analysis provided similar results, characterized by acceptable accuracy. On the contrary, methods that rely on just one of the elements that contribute to functional size tend to be quite inaccurate. In general, different methods showed different accuracy for Real-Time and non Real-Time applications. 2) It is possible to write progressively more detailed and complete UML models of user requirements that provide the data required by the simplified COSMIC methods. These models yield progressively more accurate measures of the modeled software. Initial measures are based on simple models and are obtained quickly and with little effort. As V models grow in completeness and detail, the measures increase their accuracy. Developers that use UML for requirements modeling can obtain early estimates of the applications\u2018 sizes at the beginning of the development process, when only very simple UML models have been built for the applications, and can obtain increasingly more accurate size estimates while the knowledge of the products increases and UML models are refined accordingly. 3) Both Function Point Analysis and COSMIC functional size measures appear correlated to object-oriented measures. In particular, associations with basic object- oriented measures were found: Function Points appear associated with the number of classes, the number of attributes and the number of methods; CFP appear associated with the number of attributes. This result suggests that even a very basic UML model, like a class diagram, can support size measures that appear equivalent to functional size measures (which are much harder to obtain). Actually, object-oriented measures can be obtained automatically from models, thus dramatically decreasing the measurement effort, in comparison with functional size measurement. In addition, we proposed conversion method between Function Points and COSMIC based on analytical criteria. Our research has expanded the knowledge on how to simplify the methods for measuring the functional size of the software, i.e., the measure of functional user requirements. Basides providing information immediately usable by developers, the researchalso presents examples of analysis that can be replicated by other researchers, to increase the reliability and generality of the results

Effort Estimation Methods in Software Development Using Machine Learning Algorithms

Estimation of effort for the proposed software is a standout amongst the most essential activities in project management. Proper estimation of effort is often desirable in order to avoid any sort of failures in a project and is the practice to adopted by developers at the very beginning stage of the software development life cycle. Estimating the effort and schedule with a higher accuracy is a challenge that attracts attention of researchers as well as practitioners. Predicting the effort required to develop a software to a certain level of accuracy is definitely a difficult assignment for a manager or system analyst, when the requirements are not very clearly identified. Effort estimation helps project managers to determine time and effort required for the successful completion of the project. In order to help the organization in developing qualitative products within a planned time frame, the job of appropriate software effort estimation is of primary requirement. For measuring the cost and effort of software development, traditional software estimation techniques like Constructive Cost Estimation (COCOMO) model and Function Point Analysis (FPA) have not been proved very much satisfactory, because of uncertainties associated with parameters such as Line Of Code (LOC) and Function Point (FP) respectively, used for procedural programming concept. The procedural oriented design splits the data and procedure, whereas accepted practice of present day i.e., the object-oriented design combines both of them Since class and use case are the basic logical units of an object-oriented system, the use of Class Point (CP) and Use Case Point (UCP) approach to estimate the project effort helps to get more accurate result. For projects based on the aspect of Web Engineering, effort estimation practice is identified as a critical issue Considering these facts, there is a strong need for formal estimation of web-based projects, which can be accomplished by the help of International Software Benchmarking Standards Group (ISBSG) dataset. Similarly, in case of agile projects, Story Point Approach (SPA) is used to measure the effort required to implement a user story. By adding up the estimates of user stories which were nished during an iteration (story point iteration), the project velocity is obtained. The dataset related to CP, UCP and SPA are collected from previous projects mentioned in few research articles or from industries in order to assess the results. In order to create results of estimation with more accuracy, when managing issues of complex connections in the middle of inputs as well as yields, and where, there is a distortion in the inputs by high noise levels, the application of machine learning (ML) techniques helps to bring out results with more accuracy. A number of past research studies indicate that no single technique turns out to be the best for all cases. This is because of the dependency of system's execution altogether on the predicted function types, variations in properties of collected data, number of tests, noise ratio and so on. Hence the use of ML techniques in order to cope with issues arises in real-life situation is considered to be worthwhile. The research work carried out here presents the use of various ML techniques for software effort estimation using CP, UCP, Web-based and SPA approaches. The ML techniques are implemented taking into consideration of related dataset to predict the required effort

Refactoring Legacy JavaScript Code to Use Classes: The Good, The Bad and The Ugly

JavaScript systems are becoming increasingly complex and large. To tackle the challenges involved in implementing these systems, the language is evolving to include several constructions for programming- in-the-large. For example, although the language is prototype-based, the latest JavaScript standard, named ECMAScript 6 (ES6), provides native support for implementing classes. Even though most modern web browsers support ES6, only a very few applications use the class syntax. In this paper, we analyze the process of migrating structures that emulate classes in legacy JavaScript code to adopt the new syntax for classes introduced by ES6. We apply a set of migration rules on eight legacy JavaScript systems. In our study, we document: (a) cases that are straightforward to migrate (the good parts); (b) cases that require manual and ad-hoc migration (the bad parts); and (c) cases that cannot be migrated due to limitations and restrictions of ES6 (the ugly parts). Six out of eight systems (75%) contain instances of bad and/or ugly cases. We also collect the perceptions of JavaScript developers about migrating their code to use the new syntax for classes.Comment: Paper accepted at 16th International Conference on Software Reuse (ICSR), 2017; 16 page