Effect of software evolution on metrics and applicability of Lehman's laws of software evolution

Singh, Gagandeep; Singh, Hardeep

doi:10.1145/2413038.2413046

Cited by 4 publications

(12 citation statements)

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“…Singh and Singh [22] studied two non-agile open source projects and the applicability of all eight of Lehman's laws. They found that the laws related to increasing complexity and continuous growth were validated by the metrics.…”

Section: Related Workmentioning

confidence: 99%

“…• M1: Number of LOC (per release) [10], [22] • M2: Number of files (per release) [10] Two different metrics were used initially to capture different aspects of the system's growth and study it from multiple perspectives. However, we determined that the two metrics are strongly correlated at 0.97 for both projects, and therefore they can be used interchangeably with the same meaning.…”

Section: A Growth and Changementioning

confidence: 99%

“…We follow the approach of Singh and Singh [22] in measuring complexity and used the following metrics to measure our projects' complexity:…”

Section: B Increasing Complexitymentioning

confidence: 99%

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Lehman's laws in agile and non-agile projects

Duran

Burns

Snell

2013

2013 20th Working Conference on Reverse Engineering (WCRE)

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Software team leaders and managers must make decisions on what type of process model they will use for their projects. Recent work suggests the use of agile processes since they promote shorter development cycles, better collaboration, and process flexibility. Due to the many benefits of agile processes, many software organizations have shifted to using more agile process methodologies. However, there is limited research that studies how agile processes affects the evolution of a software system over time.In this paper, we perform an empirical study to better understand the effects of using agile processes. We compare two open source projects, one of which uses a tailored agile process (i.e., Xtreme Programming) and another that has no formal process methodology. In particular, we compare the two projects within the context of Lehmans Laws for continuing growth, continuing change, increasing complexity, and conservation of familiarity. Our findings show that all four of the laws held true for the project that uses an agile process and that there are noticeable differences in the evolution of the two projects, many of which can be traced back to specific practices used by the agile team.

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Section: Related Workmentioning

confidence: 99%

Section: A Growth and Changementioning

confidence: 99%

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Lehman's laws in agile and non-agile projects

Duran

Burns

Snell

2013

2013 20th Working Conference on Reverse Engineering (WCRE)

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“…Software often needs to be maintained in order to add new functionalities and fix remaining faults [2]. Ad-hoc maintenance produces legacy software that is too large and complex [3]. For developers, this legacy software is hard to understand and modify, which makes faults fixing more difficult.…”

Section: Introductionmentioning

confidence: 99%

“…These are collectively known as bad smells [4]. Bad smell removal makes software easier to understand and modify [3], [5], increases software analyzability, and decreases the time needed to analyze faults. Although there are several types of bad smell, this paper addresses only the long method bad smell since it occurs most frequently [6] and causes other bad smells, such as large class [7].…”

Section: Introductionmentioning

confidence: 99%

Refactoring Opportunity Identification Methodology for Removing Long Method Smells and Improving Code Analyzability

Meananeatra

Rongviriyapanish

Apiwattanapong

2018

IEICE Trans. Inf. & Syst.

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SUMMARYAn important step for improving software analyzability is applying refactorings during the maintenance phase to remove bad smells, especially the long method bad smell. Long method bad smell occurs most frequently and is a root cause of other bad smells. However, no research has proposed an approach to repeating refactoring identification, suggestion, and application until all long method bad smells have been removed completely without reducing software analyzability. This paper proposes an effective approach to identifying refactoring opportunities and suggesting an effective refactoring set for complete removal of long method bad smell without reducing code analyzability. This approach, called the long method remover or LMR, uses refactoring enabling conditions based on program analysis and code metrics to identify four refactoring techniques and uses a technique embedded in JDeodorant to identify extract method. For effective refactoring set suggestion, LMR uses two criteria: code analyzability level and the number of statements impacted by the refactorings. LMR also uses side effect analysis to ensure behavior preservation. To evaluate LMR, we apply it to the core package of a real world java application. Our evaluation criteria are 1) the preservation of code functionality, 2) the removal rate of long method characteristics, and 3) the improvement on analyzability. The result showed that the methods that apply suggested refactoring sets can completely remove long method bad smell, still have behavior preservation, and have not decreased analyzability. It is concluded that LMR meets the objectives in almost all classes. We also discussed the issues we found during evaluation as lesson learned. key words: code analyzability, long method bad smell, refactoring, software engineering, software maintenance

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The evolution of mobile apps: an exploratory study

Zhang

Sagar

Shihab

2013

Proceedings of the 2013 International Workshop on Software Development Lifecycle for Mobile

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As mobile apps continue to grow in popularity, it is important to study their evolution. Lehman's laws of software evolution have been proposed and used to study the evolution of traditional, large software systems (also known as desktop apps). However, do Lehman's laws of software evolution hold for mobile apps?, especially since developing mobile apps presents different challenges compared to the development of desktop apps.In this paper, we examine the applicability of three of Lehman's laws on mobile apps. In particular, we focused on three laws: the law of continuing change, increasing complexity, and declining quality. We extracted a number of metrics and performed a case study on two applications: VLC and ownCloud. Our findings show that the law of continuing change and declining quality seem to apply for mobile apps, however, we find different outcomes for the law of increasing complexity. Then, we compare the mobile app version to the desktop version and find that the two versions follow the same trends for the law of continuing change. On the contrary, the desktop and mobile version have different trends for the law of increasing complexity and the law of declining quality.

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Effect of software evolution on metrics and applicability of Lehman's laws of software evolution

Cited by 4 publications

References 0 publications

Lehman's laws in agile and non-agile projects

Lehman's laws in agile and non-agile projects

Refactoring Opportunity Identification Methodology for Removing Long Method Smells and Improving Code Analyzability

The evolution of mobile apps: an exploratory study

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