The Defected Story Points Model in Extreme Programming

2025/08/27 23:00

Abstract and 1. Introduction

  1. Background and 2.1. Related Work

    2.2. The Impact of XP Practices on Software Productivity and Quality

    2.3. Bayesian Network Modelling

  2. Model Design

    3.1. Model Overview

    3.2. Team Velocity Model

    3.3. Defected Story Points Model

  3. Model Validation

    4.1. Experiments Setup

    4.2. Results and Discussion

  4. Conclusions and References

3.3. Defected Story Points Model

This model calculates an estimate number for the defected story points to be redeveloped in the next release. This number is affected by two XP practices: Test Driven development and Onsite Customer practices. Different components of the model are described as follows:

\

  • Dev. Productivity: The developer productivity measured as the number Line Of Code (LOC) per day. According to the literature [4], a normal distribution with mean 40 and Standard Deviation of 20 represents this value.

    \

  • Estimated Release KLOC: represents the number of KLOC produced from this release. This value is calculated as the product of multiplying Dev. Productivity times Team size times Estimated Release Days.

    \

  • Defect Injection Ratio: represents the number of defects per KLOC. This value was set to a normal distribution with mean 20 and standard deviation 5 [4].

    \

  • Defect Rate: represents the number of defects in this release. It is calculated as the multiplication of the Estimated Release KLOC times Defect Injection Ratio.

    \

  • Defected Story Points: This value represents the number of defected story points to be re-developed in the next release taking into account the impact of two XP practices: Test Driven development and Onsite Customer practices (Equation 3). OSCImpactFactor and TDDImpactFactor represent the impact of the Onsite Customer and Test Driven development practices on reducing the defect rate. According to the literature, there values were set to 0.8 and 0.4 respectively [3],[4]. More details regarding the impact of these practices in the defect rate are available in the Background section.

\ DefectedStoryPoints = DefectRate*(1- OSCImpactFactor * onsitecustomerusage )*(1 TDDImpactFactor *tddusage) Equation (3)

\ Figure 5 Defected Story Points Model

\

:::info Authors:

(1) Mohamed Abouelelam, Software System Engineering, University of Regina, Regina, Canada;

(2) Luigi Benedicenti, Software System Engineering, University of Regina, Regina, Canada.

:::


:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.

:::

\

Clause de non-responsabilité : les articles republiés sur ce site proviennent de plateformes publiques et sont fournis à titre informatif uniquement. Ils ne reflètent pas nécessairement les opinions de MEXC. Tous les droits restent la propriété des auteurs d'origine. Si vous estimez qu'un contenu porte atteinte aux droits d'un tiers, veuillez contacter service@support.mexc.com pour demander sa suppression. MEXC ne garantit ni l'exactitude, ni l'exhaustivité, ni l'actualité des contenus, et décline toute responsabilité quant aux actions entreprises sur la base des informations fournies. Ces contenus ne constituent pas des conseils financiers, juridiques ou professionnels, et ne doivent pas être interprétés comme une recommandation ou une approbation de la part de MEXC.
Partager des idées

Vous aimerez peut-être aussi

CFTC to Surveil Crypto, Prediction Markets Using Nasdaq Platform

CFTC to Surveil Crypto, Prediction Markets Using Nasdaq Platform

The post CFTC to Surveil Crypto, Prediction Markets Using Nasdaq Platform appeared on BitcoinEthereumNews.com. In brief The CFTC will start using Nasdaq’s Market Surveillance platform to enhance its ability to detect fraud and market manipulation in crypto and production markets. The shift comes as lawmakers mull the CLARITY Act. A White House report recently recommended that the CFTC impose requirements on reporting market data for certain crypto firms. The Commodity Futures Trading Commission is stepping up efforts to surveil financial markets, tapping technology from Nasdaq to gain a more granular view of crypto transactions, according to a press release published by the regulator on Wednesday. Nasdaq’s Market Surveillance platform, which covers a dozen asset classes, including digital assets and prediction markets, represents a significant upgrade, the CFTC said, as it moves to replace its “‘90s-era legacy system” for detecting illicit behavior among market participants. Prediction markets have been buzzy, with the president’s son joining Polymarket’s advisory board on Tuesday. Still, a Nasdaq spokesperson told Decrypt that prediction markets mirror derivatives that the CFTC has regulated since the agency was established in 1974. “Prediction markets operate in the same way as most derivative markets, with similar potential for market abuse and manipulation,” the spokesperson said. “The technology can therefore be adapted to serve almost all forms of event-based markets.”  At the same time, the CFTC acknowledged that markets have changed rapidly in recent years, with digital infrastructure providing round-the-clock trading. “The growth in both traditional and new markets and products, combined with innovations in market structure, such as the launch of continuous trading hours, require increasingly sophisticated tools to prevent and detect potential market abuse,” the CFTC said. The shift also comes as U.S. lawmakers mull the CLARITY Act, a comprehensive piece of crypto legislation that would establish jurisdiction between the U.S. Securities and Exchange Commission and the CFTC.  The bill was passed in the U.S. House…
Partager
BitcoinEthereumNews2025/08/28 06:05
Partager