The Mythical Lines of Code Metric

Using the lines of code (LOC) metric in software project management can be a financial moral hazard to an organization. This is especially true for upper management who handles an organizational budget and strategic plan. Software project managers have their own budgets. However, if they fail to meet the budget, the organization's cash flow, rather than the project manager's personal cash flow, will suffer. This chapter will discuss the practice of software project management, the field of software metrics, game theory, and the game theory issue of moral hazard. It will demonstrate why using LOC as a metric can present a moral hazard to senior management and an organization.

Use of Software Metrics to Improve the Quality of Software Projects Using Regression Testing

Testing of software is broadly divided into three types i.e., code based, model based and specification based. To find faults at early stage, model based testing can be used in which testing can be started from design phase. Furthermore, in this chapter, to generate new test cases and to ensure the quality of changed software, regression testing is used. Early detection of faults will not only reduce the cost, time and effort of developers but also will help finding risks. We are using structural metrics to check the effect of changes made to software. Finally, the authors suggest identifying metrics and analyze the results using NDepend simulator. If results show deviation from standards then again perform regression testing to improve the quality of software.

Hybrid Representation to Locate Vulnerable Lines of Code

Locating vulnerable lines of code in large software systems needs huge efforts from human experts. This explains the high costs in terms of budget and time needed to correct vulnerabilities. To minimize these costs, automatic solutions of vulnerabilities prediction have been proposed. Existing machine learning (ML)-based solutions face difficulties in predicting vulnerabilities in coarse granularity and in defining suitable code features that limit their effectiveness. To addressee these limitations, in the present work, the authors propose an improved ML-based approach using slice-based code representation and the technique of TF-IDF to automatically extract effective features. The obtained results showed that combining these two techniques with ML techniques allows building effective vulnerability prediction models (VPMs) that locate vulnerabilities in a finer granularity and with excellent performances (high precision (>98%), low FNR (<2%) and low FPR (<3%) which outperforms software metrics and are equivalent to the best performing recent deep learning-based approaches.

Symfony and Laravel – a comparative analysis of PHP programming frameworks

This paper is a comparative analysis of PHP programming frameworks - Symfony and Laravel. The analysis was conducted on two test applications prepared for this purpose, based on the latest versions of the following technologies: Symfony 5.2 and Laravel 8. Both applications, being simple auction systems, have the same set of functionalities. They were compared in terms of selected criteria. Their implementation process, software metrics, performance and amount of community support were compared. Apache jMeter was used for performance testing. With its help, tests of several operations on databases were performed. The operations were as follows: adding auctions, retrieving auction details, editing, deleting auctions, bidding on an auction and simultaneous closing 1,000 auctions. The test results for the selected criteria were significantly better for the Laravel framework based application.

Identifying ”good” practices of developers using corrective and preventive actions and their impact on software metrics

This work presents a literature review, an analysis on how certain actions of software developers impact certain software metrics (such as defect density), and an attempt to highlight good (most efective in terms of software metrics) development practices based on the corrective and preventive actions extracted from the literature. Across multiple relevant studies, defect density was the most used metric, that is why this metric was used to identify the good practices. The most used practices are those that were encountered the most during data extraction from the relevant literature. The extracted actions were categorized using CMMI taxonomy. Overall, 115 unique actions were identifed falling into 53 CMMI taxonomy categories. There were 30 good and the most used practices identifed that fell into 4 CMMI categories.

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data. Several studies investigate how software metrics are used and how modeling techniques influence prediction performance. However, the software’s metrics diversity impact on the predictor remains unclear. Thus, this paper aims to assess the impact of various metric sets on CPDP and investigate the feasibility of CPDP with hybrid metrics. Based on four software metrics types, we investigate the impact of various metric sets on CPDP in terms of F-measure and statistical methods. Then, we validate the dominant performance of CPDP with hybrid metrics. Finally, we further verify the CPDP-OSS feasibility built with three types of metrics (orient-object, semantic, and structural metrics) and challenge them against two current models. The experimental results suggest that the impact of different metric sets on the performance of CPDP is significantly distinct, with semantic and structural metrics performing better. Additionally, trials indicate that it is helpful for CPDP to increase the software’s metrics diversity appropriately, as the CPDP-OSS improvement is up to 53.8%. Finally, compared with two baseline methods, TCA+ and TDSelector, the optimized CPDP model is viable in practice, and the improvement rate is up to 50.6% and 25.7%, respectively.

Novel Approach for software metrics Sharing

Сбор метрик программного обеспечения является фундаментальной деятельностью, которая необходима для проведния практически любого эмпирического исследования в области программной инженерии. Однако, даже при наличии широкого спектра инструментов, сбор таких фундаментальных данных по-прежнему занимает много времени. Более того, каждый исследователь собирает практически одни и те же данные (например, метрики CK, цикломатическая сложность МакКейба и т.д.) из практически одних и тех же проектов (например, из известных проектов с открытым исходным кодом). Объем такой дублирующей работы, выполняемой в сообществе, уменьшает усилия, которые исследователи могут потратить на наиболее ценную часть своих исследований, такую как разработка новых теорий и моделей и их эмпирическая оценка. В данной работе предлагается новый подход для сбора и обмена данными метрик программного обеспечения, позволяющий сотрудничать исследователям и сократить количество напрасных усилий в сообществе разработчиков программного обеспечения. Мы стремимся достичь этой цели, предлагая Формат обмена программными метриками (SMEF)и REST API для сбора, хранения и обмена данными метрик программного обеспечения. In almost every empirical software engineering study, software metrics collection is a fundamental activity. Although many tools exist to collect this data, it still takes a considerable amount of time. In addition, almost all researchers collect essentially the same data (e.g., CK metrics, McCabe Cyclomatic Complexity, etc.) from essentially the same sources (e.g., well-known open-source projects).Having so much duplication of work done within a community reduces the amount of time that researchers can spend developing new ideas and evaluating them empirically, which is the most valuable part of their research. In this paper, we propose a novel approach for getting and sharing software metrics data that will allow them to collaborate and reduce the amount of wasted effort. SMEF, a file format for exchanging software metrics information, and a REST API, targeted at this objective, are proposed in this paper.

Hybrid Maintainability Prediction using Soft Computing Techniques

Effective software maintenance is a crucial factor to measure that can be achieved with the help of software metrics. In this paper, authors derived a new approach for measuring the maintainability of software based on hybrid metrics that takes advantages of both i.e. static metrics and dynamic metrics in an object-oriented environment whereas, dynamic metrics capture the run time features of object-oriented languages i.e. run time polymorphism, dynamic binding etc. which is not covered by static metrics. To achieve this, the authors proposed a model based on static and hybrid metrics to measure maintainability factor by using soft computing techniques and it is found that the proposed neuro-fuzzy model was trained well and predict adequate results with MAE 0.003 and RMSE 0.009 based on hybrid metrics. Additionally, the proposed model was validated on two test datasets and it is concluded that the proposed model performed well, based on hybrid metrics.