本文選題：移動(dòng)App + 應(yīng)用商店�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的興起和智能手機(jī)的流行,市場(chǎng)上已經(jīng)有數(shù)以百萬(wàn)計(jì)的移動(dòng)App可以安裝在用戶手機(jī)上并為其提供服務(wù)。用戶通過(guò)應(yīng)用商店下載、使用這些App,同時(shí)在應(yīng)用商店中以評(píng)論的方式對(duì)App的質(zhì)量進(jìn)行反饋。用戶的評(píng)論對(duì)于App開發(fā)者至關(guān)重要。開發(fā)者對(duì)App進(jìn)行更新時(shí)除了要考慮App本身的發(fā)展需求,更需要考慮用戶的需求與感受。將高質(zhì)量評(píng)論中所體現(xiàn)的信息融入到App更新中將有助于提高App的質(zhì)量和評(píng)級(jí)。然而目前為止,尚未有明確研究表明開發(fā)者是否利用、以何種程度利用用戶評(píng)論信息對(duì)自身App進(jìn)行改進(jìn),從而提升服務(wù)質(zhì)量。針對(duì)以上問題,本文通過(guò)提取用戶評(píng)論和App更新日志中的特征并識(shí)別它們之間的潛在關(guān)系,利用對(duì)原子更新單元(Atomic Update Unit,abbr.AU)進(jìn)行聚類的辦法發(fā)現(xiàn)了7種更新模式(Update Pattern,abbr.UP)。更新模式體現(xiàn)了開發(fā)者以何種強(qiáng)度在何種及時(shí)程度以及充分程度上對(duì)用戶請(qǐng)求進(jìn)行響應(yīng)的一種共性行為模式。同時(shí),針對(duì)更新模式進(jìn)行了一系列的實(shí)證研究。本文的結(jié)論幫助開發(fā)人員清楚地了解到自身對(duì)用戶評(píng)論進(jìn)行反饋的習(xí)慣,對(duì)開發(fā)人員如何充分利用用戶評(píng)論提升App質(zhì)量提供了建議。具體研究?jī)?nèi)容包括以下幾個(gè)部分:(1)實(shí)現(xiàn)了一種針對(duì)Google Play應(yīng)用商店上App的更新日志和評(píng)論的自動(dòng)化收集工具。詳細(xì)介紹了工具在云端的部署和維護(hù)的過(guò)程。定義了待收集App數(shù)據(jù)的數(shù)據(jù)模型。對(duì)收集到的數(shù)據(jù)進(jìn)行了統(tǒng)計(jì)層面的分析并得到了一些統(tǒng)計(jì)結(jié)果。(2)定義了原子更新單元(Atomic Update Unit,abbr.AU)并介紹了其生成方法,給出了針對(duì)原子更新單元的及時(shí)性、充分性、特征更新強(qiáng)度、用戶特征請(qǐng)求強(qiáng)度變化趨勢(shì)的計(jì)算方法。設(shè)計(jì)了一種針對(duì)用戶特征請(qǐng)求強(qiáng)度/特征更新強(qiáng)度變化趨勢(shì)(Intensity Trend Chart for Feature Request and Update,abbr.TC)的分段擬合歸一化算法。(3)定義了更新模式(Update Pattern,abbr.UP)。給出了挖掘更新模式的方法,挖掘得到了七種更新模式并對(duì)其進(jìn)行分析。(4)進(jìn)行了一系列實(shí)證研究,得到了一些結(jié)論:App開發(fā)者對(duì)某特征進(jìn)行更新時(shí)采用哪種模式很大程度上取決于開發(fā)者本身喜好而不是該特征的本質(zhì);開發(fā)者采用更新的穩(wěn)定性存在明顯分化,約有65%的App的的更新穩(wěn)定性處于較較不穩(wěn)定狀態(tài),與此同時(shí),有12%的App的更新穩(wěn)定性處于非常穩(wěn)定狀態(tài),這部分開發(fā)者更傾向于對(duì)自身App的同一特征在歷史更新中采用相同的模式;發(fā)現(xiàn)了兩種模式與App評(píng)論數(shù)量有顯著正相關(guān),發(fā)現(xiàn)三種更新模式與App在應(yīng)用商店中的排名有負(fù)相關(guān)。發(fā)現(xiàn)了一種更新模式與App的評(píng)分有正相關(guān)。
[Abstract]:With the rise of the Internet and the popularity of smartphones, there are already millions of mobile App on the market can be installed on user phones and provide services. Users download it from the app store, use it, and comment on the quality of App in the app store. User comments are critical to App developers. Developers need to consider not only the development needs of App itself, but also the needs and feelings of users when updating App. Incorporating the information embodied in high-quality reviews into App updates will help improve the quality and rating of App. However, up to now, there is no clear research on whether or not developers use the user comment information to improve their App, so as to improve the quality of service. In order to solve the above problems, by extracting the features from user comments and App update logs and recognizing the potential relationships between them, seven update patterns (update abbr.up) are found by clustering Atomic Update unit abbr.AU. The update pattern reflects a common behavior pattern in which the developer responds to the user's request in what degree of timeliness and adequacy. At the same time, a series of empirical studies on the renewal model are carried out. The conclusion of this paper helps developers to understand their habit of feedback on user reviews and provides suggestions on how to make full use of user reviews to improve the quality of App. The main contents of this paper are as follows: (1) an automatic collection tool for App update logs and comments in the Google play App Store is implemented. The deployment and maintenance of the tool in the cloud are introduced in detail. The data model of App data to be collected is defined. The collected data are analyzed at the statistical level and some statistical results are obtained. (2) the Atomic Update unit (AU) is defined and its generating method is introduced, and the timeliness, adequacy and characteristic update intensity of the atomic update unit are given. The calculation method of intensity change trend of user feature request. A segmented fitting normalization algorithm for intensity trend Chart for feature request and Updateabbr.TC is designed. (3) Update pattern is defined. In this paper, the method of mining update patterns is given, and seven updating patterns are obtained and analyzed. (4) A series of empirical studies are carried out. Some conclusions are drawn: the pattern used by App developers to update a feature depends to a large extent on the nature of the feature rather than the nature of the feature; the stability of the developer's adoption of the update is clearly divided. About 65% of the App have relatively unstable renewal stability, while 12% of the App are in a very stable state. This part of developers tend to use the same pattern for the same characteristics of their own App in the historical update, and found that the two models have significant positive correlation with the number of App reviews, and the three update patterns have negative correlation with the ranking of App in the application store. A positive correlation was found between an update model and the App score.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.56

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